Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is no jump in computational complexity. (...) Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability. (shrink)

The article is a plea for ethicists to regard probability as one of their most important concerns. It outlines a series of topics of central importance in ethical theory in which probability is implicated, often in a surprisingly deep way, and lists a number of open problems. Topics covered include: interpretations of probability in ethical contexts; the evaluative and normative significance of risk or uncertainty; uses and abuses of expected utility theory; veils of ignorance; Harsanyi’s aggregation theorem; (...) population size problems; equality; fairness; giving priority to the worse off; continuity; incommensurability; nonexpected utility theory; evaluative measurement; aggregation; causal and evidential decision theory; act consequentialism; rule consequentialism; and deontology. (shrink)

When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this (...) causal superseding effect is dependent on a particular event structure, following a prediction of our counterfactual model. Experiment 4 demonstrates that causal superseding can occur with violations of non-moral norms. We propose a model of the superseding effect based on the idea of counterfactual sufficiency. (shrink)

Evidential decision theory (EDT) says that the choiceworthiness of an option depends on its evidential connections to possible outcomes. Causal decision theory (CDT) holds that it depends on your beliefs about its causal connections. While Newcomb cases support CDT, Arif Ahmed has described examples that support EDT. A new account is needed to get all cases right. I argue that an option A's choiceworthiness is determined by the probability that a good outcome ensues at possible A‐worlds that match actuality (...) in the facts causally unaffected by your decision (the “unaffected facts”). Moreover, you should evaluate A on the assumption that A is compossible with the unaffected facts. This view entails that you should use EDT when evaluating A on the assumption that the unaffected facts determine your action, but use CDT when assessing A on the opposite assumption. A's choiceworthiness equals a weighted average of these conditional assessments. The weights are determined by your beliefs about whether the unaffected fact determine your action. This account gets both Newcomb and Ahmed cases right. According to an influential view, whether you take the unaffected facts to determine your action can make a difference to whether you can regard yourself as free and the action as being under your control. While my account is neutral on this issue, it entails that whether you take the unaffected facts to determine your action is important in a different way: it matters to whether you should follow EDT or CDT. (shrink)

Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as (...) a weighted average of the probability of C in those submodels that truthmake A∨ B (Briggs 2012; Fine 2016, 2017). The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined. (shrink)

"Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational (...) data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. (shrink)

Many approaches to quantum gravity -the theory that should account for quantum and gravitational phenomena under the same theoretical umbrella- seem to point at some form of spacetime emergence, i.e., the fact that spacetime is not a fundamental entity of our physical world. This tenet has sparked many philosophical discussions: from the so-called empirical incoherence problem to different accounts of emergence and mechanisms thereof. In this contribution, I focus on the partial order relation of causal set theory and argue that (...) causation can be characterized as an a-temporal constraint over the kinematic space defined by the theory. The relation constrains the growth of a new element/event with respect to the other elements/events of a given set. Therefrom, the flow of time emerges from the collection of the possible growths of the given set, where each possibility is characterized by a classical probability assigned by the dynamics of the theory. (shrink)

Agent-causal libertarians maintain we are irreducible agents who, by acting, settle matters that aren’t already settled. This implies that the neural matters underlying the exercise of our agency don’t conform to deterministic laws, but it does not appear to exclude the possibility that they conform to statistical laws. However, Pereboom (Noûs 29:21–45, 1995; Living without free will, Cambridge University Press, Cambridge, 2001; in: Nadelhoffer (ed) The future of punishment, Oxford University Press, New York, 2013) has argued that, if these neural (...) matters conform to either statistical or deterministic physical laws, the complete conformity of an irreducible agent’s settling of matters with what should be expected given the applicable laws would involve coincidences too wild to be credible. Here, I show that Pereboom’s argument depends on the assumption that, at times, the antecedent probability certain behavior will occur applies in each of a number of occasions, and is incapable of changing as a result of what one does from one occasion to the next. There is, however, no evidence this assumption is true. The upshot is the wild coincidence objection is an empirical objection lacking empirical support. Thus, it isn’t a compelling argument against agent-causal libertarianism. (shrink)

I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many have rejected this thesis because it seems to entail that defendants can be found liable solely on the basis of statistical evidence. I argue that this inference is invalid. I do so by developing a view, called Legal Causalism, that combines Thomson's (1986) causal analysis of evidence with recent work in formal theories of causal inference. On this view, legal standards of proof can (...) be reduced to probabilities, but deriving these probabilities involves more than just statistics. (shrink)

Abstract In this paper I consider an easier-to-read and improved to a certain extent version of the causal chance-based analysis of counterfactuals that I proposed and argued for in my A Theory of Counterfactuals. Sections 2, 3 and 4 form Part I: In it, I survey the analysis of the core counterfactuals (in which, very roughly, the antecedent is compatible with history prior to it). In section 2 I go through the three main aspects of this analysis, which are the (...) following. First, it is a causal analysis, in that it requires that intermediate events to which the antecedent event is not a cause be preserved in the main truth-condition schema. Second, it highlights the central notion to the semantics of counterfactuals on the account presented here -- the notion of the counterfactual probability of a given counterfactual, which is the probability of the consequent given the following: the antecedent, the prior history, and the preserved intermediate events. Third, it considers the truth conditions for counterfactuals of this sort as consisting in this counterfactual probability being higher than a threshold. In section 3, I re-formulate the analysis of preservational counterfactuals in terms of the notion of being a cause, which ends up being quite compact. In section 4 I illustrate this analysis by showing how it handles two examples that have been considered puzzling – Morgenbesser's counterfactual and Edgington's counterfactual. Sections 5 and on constitute Part II: Its main initial thrust is provided in section 5, where I present the main lines of the extension of the theory from the core counterfactuals (analyzed in part I) to counterfactuals (roughly) whose antecedents are not compatible with their prior history. In this part II, I elaborate on counterfactuals that don't belong to the core, and more specifically on so-called reconstructional counterfactuals (as opposed to the preservational counterfactuals, which constitute the core counterfactual-type). The heart of the analysis is formulated in terms of processes leading to the antecedent (event/state), and more specifically in terms of processes likely to have led to the antecedent, a notion which is analyzed entirely in terms of chance. It covers so-called reconstructional counterfactuals as opposed to the core, so-called preservational counterfactuals, which are analyzed in sections 2 and 3 of part I. The counterfactual probability of such reconstructional counterfactuals is determined via the probability of possible processes leading to the antecedent weighed, primarily and roughly, by the conditional probability of the antecedent given such process: The counterfactual probability is thus, very roughly, a weighted sum for all processes most likely to have led to the antecedent, diverging at a fixed time. In section 6 I explain and elaborate further on the main points in section 5. In section 7 I illustrate the reconstructional analysis. I specify counterfactuals which are so-called process-pointers, since their consequent specifies stages in processes likely to have led to their antecedent. I argue that so-called backtracking counterfactuals are process-pointers counterfactuals, which fit into the reconstructional analysis, and do not call for a separate reading. I then illustrate cases where a speaker unwittingly employs a certain counterfactual while charitably construable as intending to assert (or ‘having in mind’) another. Here I also cover the issue of how to construe what one can take as back-tracking counterfactuals, or counterfactuals of the reconstructional sort, and more specifically, which divergence point they should be taken as alluding to (prior to which the history is held fixed). Some such cases also give rise to what one can take as a dual reading of a counterfactual between preservational and reconstructional readings. Such cases may yield an ambiguity, where in many cases one construal is dominant. In section 8 I illustrate the analysis by applying it to the famous Bizet-Verdi counterfactuals. This detailed analysis of counterfactuals (designed for the indeterministic case) has three main distinctive elements: its being chance-based, its causal aspect, and the use it makes of processes most likely to have led to the antecedent-event. This analysis is couched in a very different conceptual base from, and is an alternative account to, analyses in terms of the standard notion of closeness or distance of possible worlds, which is the main feature of the Stalnaker-Lewis-type analyses of counterfactuals. This notion of closeness or distance plays no role whatsoever in the analysis presented here. (This notion of closeness has been left open by Stalnaker, and to significant extent also by Lewis's second account.) . (shrink)

Within the causal modeling literature, debates about the Causal Faithfulness Condition have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to treat that parameter (...) as a function of a variable in an augmented model. By combining this proposal with widely adopted principles regarding which variables must be included in a model, I argue that the various proposed counterexamples to CFC involving coordinated parameters are not genuine counterexamples. I then consider the cases in which CFC fails due not to coordination, but by coincidence, and propose explanatory and predictive bases for ruling out such coincidences without presuming that they are improbable. The aim of the proposed defenses is not to show that CFC never fails, but rather to argue that its use in a particular context may be defended using general modeling assumptions rather than by relying on claims about how often it fails. (shrink)

It is difficult to extract reliable criteria for causal locality from the limited ingredients found in textbook quantum theory. In the end, Bell humbly warned that his eponymous theorem was based on criteria that “should be viewed with the utmost suspicion.” Remarkably, by stepping outside the wave-function paradigm, one can reformulate quantum theory in terms of old-fashioned configuration spaces together with ‘unistochastic’ laws. These unistochastic laws take the form of directed conditional probabilities, which turn out to provide a hospitable foundation (...) for encoding microphysical causal relationships. This unistochastic reformulation provides quantum theory with a simpler and more transparent axiomatic foundation, plausibly resolves the measurement problem, and deflates various exotic claims about superposition, interference, and entanglement. Making use of this reformulation, this paper introduces a new principle of causal locality that is intended to improve on Bell's criteria, and shows directly that systems that remain at spacelike separation cannot exert causal influences on each other, according to that new principle. These results therefore lead to a general hidden-variables interpretation of quantum theory that is arguably compatible with causal locality. (shrink)

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors (...) differ in their predictive power for both causal and conditional claims. (shrink)

When probability discounting (or probability weighting), one multiplies the value of an outcome by one's subjective probability that the outcome will obtain in decision-making. The broader import of defending probability discounting is to help justify cost-benefit analyses in contexts such as climate change. This chapter defends probability discounting under risk both negatively, from arguments by Simon Caney (2008, 2009), and with a new positive argument. First, in responding to Caney, I argue that small costs and (...) benefits need to be evaluated, and that viewing practices at the social level is too coarse-grained. Second, I argue for probability discounting using a distinction between causal responsibility and moral responsibility. Moral responsibility can be cashed out in terms of blameworthiness and praiseworthiness, while causal responsibility obtains in full for any effect which is part of a causal chain linked to one's act. With this distinction in hand, unlike causal responsibility, moral responsibility can be seen as coming in degrees. My argument is, given that we can limit our deliberation and consideration to that which we are morally responsible for and that our moral responsibility for outcomes is limited by our subjective probabilities, our subjective probabilities can ground probability discounting. (shrink)

Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of (...) contrastive causal explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

A common objection to probabilistic theories of causation is that there are prima facie causes that lower the probability of their effects. Among the many replies to this objection, little attention has been given to Mellor's (1995) indirect strategy to deny that probability-lowering factors are bona fide causes. According to Mellor, such factors do not satisfy the evidential, explanatory, and instrumental connotations of causation. The paper argues that the evidential connotation only entails an epistemically relativized form of causal (...) attribution, not causation itself, and that there are clear cases of explanation and instrumental reasoning that must appeal to negatively relevant factors. In the end, it suggests a more liberal interpretation of causation that restores its connotations. Una objeción común a las teorías probabilísticas de la causalidad es que aparentemente existen causas que disminuyen la probabilidad de sus efectos. Entre las muchas respuestas a esta objeción, se le ha dado poca atención a la estrategia indirecta de D. H. Mellor (1995) para negar que un factor que disminuya la probabilidad de un efecto sea una causa legítima. Según Mellor, tales factores no satisfacen las connotaciones evidenciales, explicativas e instrumentales de la causalidad. El artículo argumenta que la connotación evidencial sólo implica una forma epistémicamente relativizada de atribución causal y no la causalidad misma, y que hay casos claros de explicación y razonamiento instrumental que deben apelar a factores negativamente relevantes. Se sugiere una interpretación más liberal de la causalidad que reinstaura sus connotaciones. (shrink)

When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder’s influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R − 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson (...) uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to Pd before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R − 1)/max(R, 1). Cc is like Pd but has normalizing property (between −1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that Pd and Cc are more reasonable than D; Cc is more useful than Pd. (shrink)

When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder’s influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R − 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson (...) uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to Pd before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R − 1)/max(R, 1). Cc is like Pd but has normalizing property (between −1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that Pd and Cc are more reasonable than D; Cc is more useful than Pd. (shrink)

There is widespread belief in a tension between quantum theory and special relativity, motivated by the idea that quantum theory violates J. S. Bell’s criterion of local causality, which is meant to implement the causal structure of relativistic space-time. This paper argues that if one takes the essential intuitive idea behind local causality to be that probabilities in a locally causal theory depend only on what occurs in the backward light cone and if one regards objective probability (...) as what imposes constraints on rational credence along the lines of David Lewis’ Principal Principle, then one arrives at the view that whether or not Bell’s criterion holds is irrelevant for whether or not local causality holds. The assumptions on which this argument rests are highlighted, and those that may seem controversial are motivated. (shrink)

This chapter focuses on the experimental practices and reasoning strategies employed in nineteenth century investigations on the causal origin of the phenomenon of Brownian movement. It argues that there was an extensive and sophisticated experimental work done on the phenomenon throughout the nineteenth century. Investigators followed as rigorously as possible the methodological standards of their time to make causal claims and advance causal explanations of Brownian movement. Two major methodological strategies were employed. The first was the experimental strategy of varying (...) the circumstances. Suspected causal factors were varied and the resulting effect on Brownian movement was studied. The main goal of this strategy was the identification of difference-making factors, i.e., factors having a causal influence on the phenomenon. The second was the so-called method of hypothesis. Rather than relying exclusively on the ability of experiments to identify difference-making factors, its proponents tried to show how the independently developed tenets of the new kinetic-molecular conception of matter provided a plausible causal explanation of Brownian movement. Each one of these methodological strategies had its distinctive practices and notions of control. None of these strategies could, on its own, establish molecular motion as the cause of Brownian movement. It was only the fruitful combination of the two strategies and of their accompanying practices and notions of control that led, at the end of the nineteenth century, to the recognition of molecular motion as the most probable cause of Brownian movement. (shrink)

This paper explores the possibility that causal decision theory can be formulated in terms of probabilities of conditionals. It is argued that a generalized Stalnaker semantics in combination with an underlying branching time structure not only provides the basis for a plausible account of the semantics of indicative conditionals, but also that the resulting conditionals have properties that make them well-suited as a basis for formulating causal decision theory. Decision theory (at least if we omit the frills) is not an (...) esoteric science, however unfamiliar it may seem to an outsider. Rather it is a systematic exposition of the consequences of certain well-chosen platitudes about belief, desire, preference and choice. It is the very core of our common-sense theory of persons, dissected out and elegantly systematized. (David Lewis, Synthese 23:331–344, 1974, p. 337). A small distortion in the analysis of the conditional may create spurious problems with the analysis of other concepts. So if the facts about usage favor one among a number of subtly different theories, it may be important to determine which one it is. (Robert Stalnaker, A Defense of Conditional Excluded Middle, pp. 87–104, 1980, p. 87). (shrink)

To justify inductive inference and vanquish classical skepticisms about human memory, external world realism, etc., Richard Fumerton proposes his “inferential internalism,” an epistemology whereby humans ‘see’ by Russellian acquaintance Keynesian probable relations (PRs) between propositions. PRs are a priori necessary relations of logical probability, akin to but not reducible to logical entailments, such that perceiving a PR between one’s evidence E and proposition P of unknown truth value justifies rational belief in P to an objective degree. A recent critic (...) of inferential internalism is Alan Rhoda, who questions its psychological plausibility. Rhoda argues that in order to see necessary relations between propositions E and P, one would need acquaintance with too many propositions at once, since our evidence E is often complex. In this paper, I criticize Rhoda’s implausibility objection as too quick. Referencing the causal status effect (CSE) from psychology, I argue that some of the complex features of evidence E contribute to our type-categorizing it as E-type, and thus we do not need to ‘see’ all of the complex features when we see the PR between E and P. My argument leaves unchanged Fumerton’s justificatory role for the PR, but enhances its psychological plausibility. (shrink)

The paper is concentrated on the special changes of the conception of causality from quantum mechanics to quantum information meaning as a background the revolution implemented by the former to classical physics and science after Max Born’s probabilistic reinterpretation of wave function. Those changes can be enumerated so: (1) quantum information describes the general case of the relation of two wave functions, and particularly, the causal amendment of a single one; (2) it keeps the physical description to be causal (...) by the conservation of quantum information and in accordance with Born’s interpretation; (3) it introduces inverse causality, “backwards in time”, observable “forwards in time” as the fundamentally random probability density distribution of all possible measurements of any physical quantity in quantum mechanics; (4) it involves a kind of “bidirectional causality” unifying (4.1) the classical determinism of cause and effect, (4.2) the probabilistic causality of quantum mechanics, and (4.3) the reversibility of any coherent state; (5) it identifies determinism with the function successor in Peano arithmetic, and its proper generalized causality with the information function successor in Hilbert arithmetic. (shrink)

Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore this possibility, delineating (...) a path from Mill’s method of agreement to Inference to the Best Explanation (IBE). (shrink)

Peter Baumann uses the Monty Hall game to demonstrate that probabilities cannot be meaningfully applied to individual games. Baumann draws from this first conclusion a second: in a single game, it is not necessarily rational to switch from the door that I have initially chosen to the door that Monty Hall did not open. After challenging Baumann's particular arguments for these conclusions, I argue that there is a deeper problem with his position: it rests on the false assumption that what (...) justifies the switching strategy is its leading me to win a greater percentage of the time. In fact, what justifies the switching strategy is not any statistical result over the long run but rather the "causal structure" intrinsic to each individual game itself. Finally, I argue that an argument by Hilary Putnam will not help to save Baumann's second conclusion above. (shrink)

A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. In this paper, I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some novel implications of incorporating into the metaphysical investigation considerations of causal psychology.

The disjunction problem and the distality problem each presents a challenge that any theory of mental content must address. Here we consider their bearing on purely probabilistic causal theories. In addition to considering these problems separately, we consider a third challenge—that a theory must solve both. We call this “the hard problem.” We consider 8 basic ppc theories along with 240 hybrids of them, and show that some can handle the disjunction problem and some can handle the distality problem, but (...) none can handle the hard problem. This is our main result. We then discuss three possible responses to that result, and argue that though the first two fail, the third has some promise. (shrink)

In this paper, I argue that causal theories of memory are typically committed to two independent, non-mutually entailing theses. The first thesis pertains to the necessity of appropriate causation in memory, specifying a condition token memories need to satisfy. The second pertains to the explanation of memory reliability in causal terms and it concerns memory as a type of mental state. Post-causal theories of memory can reject only the first (weak post-causalism) or both (strong post-causalism) theses. Upon this backdrop, I (...) examine Werning’s (2020) causalist argument from probabilistic correlation. I argue that it doesn’t establish the necessity of appropriate causation and thus it can only target strong post-causalist theories. I end up by presenting some general considerations, suggesting that memories may not always be causally linked to past experiences. (shrink)

I welcome with great enthusiasm Meling and Scheidegger’s (2023; henceforth “M&S”) timely contribution to advance an enactive approach to psychedelic therapy, especially to the complex causality involved. Their two main research questions concerned:(i) the causal interaction between the psychedelic molecule and brain activity; and (ii) the causal interaction between brain activity and the psychedelic experience. While I largely agree with and celebrate much of what is proposed by M&S, especially their employment of key enactive concepts to advance our understanding (...) of the first research question, in the following, I will present some worries regarding their answers to the second. Although I agree that there is probably a two-way reciprocal relationship between neural activity and experience, I have several points of contention regarding M&S’s proposal. My hope is to stimulate discussion on M&S’s important contribution, and to help advance a much-needed enactive science of psychedelics. (shrink)

An essay review of Richard Johns "A Theory of Physical Probability" (University of Toronto Press, 2002). Forthcoming in Studies in History and Philosophy of Science.

A theory of cognitive systems individuation is presented and defended. The approach has some affinity with Leonard Talmy's Overlapping Systems Model of Cognitive Organization, and the paper's first section explores aspects of Talmy's view that are shared by the view developed herein. According to the view on offer -- the conditional probability of co-contribution account (CPC) -- a cognitive system is a collection of mechanisms that contribute, in overlapping subsets, to a wide variety of forms of intelligent behavior. Central (...) to this approach is the idea of an integrated system. A formal characterization of integration is laid out in the form of a conditional-probabilitybased measure of the clustering of causal contributors to the production of intelligent behavior. I relate the view to the debate over extended and embodied cognition and respond to objections that have been raised in print by Andy Clark, Colin Klein, and Felipe de Brigard. (shrink)

Can heritability estimates provide causal information? This paper argues for an affirmative answer: since a non-nil heritability estimate satisfies certain characteristic properties of causation (i.e., association, manipulability, and counterfactual dependence), it increases the probability that the relation between genotypic variance and phenotypic variance is (at least partly) causal. Contrary to earlier proposals in the literature, the argument does not assume the correctness of any particular conception of the nature of causation, rather focusing on properties that are characteristic of causal (...) relationships. The argument is defended against Lewontin's (1974) locality objection and Kaplan and Turkheimer's (2021) recent critique of Genome-Wide Association Studies (GWAS). (shrink)

Rollback arguments focus on long sequences of actions with identical initial conditions in order to explicate the luck problem that indeterminism poses for libertarian free will theories (i.e. the problem that indeterministic actions appear arbitrary in a free-will undermining way). In this paper, I propose a rollback argument for probability incompatibilism, i.e. for the thesis that free will is incompatible with all world-states being governed by objective probabilities. Other than the most prominently discussed rollback arguments, this argument explicitly focusses (...) on the ability to act otherwise. It argues that the negligible probability of the relative frequencies in overall rollback patterns being relevantly different indicates that even the ability to act otherwise with regard to individual actions is not free-will enabling. My proposed argument provides probability incompatibilists with a tool to argue against a classical event-causal response to the luck problem, while it can still motivate an agent-causal response to it. (shrink)

Translating risk estimates derived from epidemiologic study into evidence of causality for a particular patient is problematic. The difficulty of this process is not unique to the medical context; rather, courts are also challenged with the task of using risk estimates to infer evidence of cause in particular cases. Thus, an examination of how this is done in a legal context might provide insight into when and how it is appropriate to use risk information as evidence of cause in (...) a medical context. A careful study of the case of Goodman v. Viljoen, a medical malpractice suit litigated in the Ontario Superior Court of Justice in 2011, reveals different approaches to how risk information is used as or might be considered a substitute for evidence of causation, and the pitfalls associated with these approaches. Achieving statistical thresholds, specifically minimizing the probability of falsely rejecting the null hypothesis, and exceeding a relative risk of 2, plays a significant role in establishing causality of the particular in the legal setting. However, providing a reasonable explanation or establishing “biological plausibility” of the causal association also seems important, and (to some) may even take precedent over statistical thresholds for a given context. (shrink)

This paper aims to scrutinize how the notion of risk should be understood and applied to possibly catastrophic cases. I begin with clarifying the standard usage of the notion of risk, particularly emphasizing the conceptual relation between risk and probability. Then, I investigate how to make decisions in the case of seemingly catastrophic disasters by contrasting the precautionary principle with the preventive (prevention) principle. Finally, I examine what kind of causal thinking tends to be actually adopted when we make (...) decisions on emergent cases. My arguments are mainly based on Japan's 2011 massive earthquake and nuclear power station accident. Masaki Ichinose. (shrink)

In most academic and non-academic circles throughout history, the world and its operation have been viewed in terms of cause and effect. The principles of causation have been applied, fruitfully, across the sciences, law, medicine, and in everyday life, despite the lack of any agreed-upon framework for understanding what causation ultimately amounts to. In this engaging and accessible introduction to the topic, Douglas Kutach explains and analyses the most prominent theories and examples in the philosophy of causation. The book is (...) organized so as to respect the various cross-cutting and interdisciplinary concerns about causation, such as the reducibility of causation, its application to scientific modeling, its connection to influence and laws of nature, and its role in causal explanation. Kutach begins by presenting the four recurring distinctions in the literature on causation, proceeding through an exploration of various accounts of causation including determination, difference making and probability-raising. He concludes by carefully considering their application to the mind-body problem. _Causation_ provides a straightforward and compact survey of contemporary approaches to causation and serves as a friendly and clear guide for anyone interested in exploring the complex jungle of ideas that surround this fundamental philosophical topic. (shrink)

There is good reason to believe that scientific realism requires a commitment to the objective modal structure of the physical world. Causality, equilibrium, laws of nature, and probability all feature prominently in scientific theory and explanation, and each one is a modal notion. If we are committed to the content of our best scientific theories, we must accept the modal nature of the physical world. But what does the scientific realist’s commitment to physical modality require? We consider whether (...) scientific realism is compatible with Humeanism about the laws of nature, and we conclude that it is not. We specifically identify three major problems for the best-systems account of lawhood: its central concept of strength cannot be formulated non-circularly, it cannot offer a satisfactory account of the laws of the special sciences, and it can offer no explanation of the success of inductive inference. In addition, Humeanism fails to be naturalistically motivated. For these reasons, we conclude that the scientific realist must embrace natural necessity. (shrink)

One thousand fair causally isolated coins will be independently flipped tomorrow morning and you know this fact. I argue that the probability, conditional on your knowledge, that any coin will land tails is almost 1 if that coin in fact lands tails, and almost 0 if it in fact lands heads. I also show that the coin flips are not probabilistically independent given your knowledge. These results are uncomfortable for those, like Timothy Williamson, who take these probabilities to play (...) a central role in their theorizing. (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing (...) explanatory factors with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks. (shrink)

The recent literature on mental causation has not been kind to nonreductive, materialist functionalism (‘functionalism’, hereafter, except where that term is otherwise qualified). The exclusion problem2 has done much of the damage, but the epiphenomenalist threat has taken other forms. Functionalism also faces what I will call the ‘problem of metaphysically necessary effects’ (Block, 1990, pp. 157-60, Antony and Levine, 1997, pp. 91-92, Pereboom, 2002, p. 515, Millikan, 1999, p. 47, Jackson, 1998, pp. 660-61). Functionalist mental properties are individuated partly (...) by their relation to the very effects those properties’ instantiations are thought to cause. Consequently, functionalist causal generalizations would seem to have the following problematical structure: The state of being, among other things, a cause of e (under such-andsuch conditions) causes e (under those conditions).3 The connection asserted lacks the contingency one would expect of a causal generalization. Mental states of the kind in question are, by metaphysical necessity, causes of e; any state that does not cause e is thereby a different kind of state. Yet, a mental state’s being the sort of state it is must play some causal role if functionalism is to account for mental causation.4 In what follows, I first articulate more fully the problem of metaphysically necessary effects. I then criticize three functionalist attempts to solve the problem directly. Given the failure of functionalist efforts to meet the problem head-on, I consider less direct strategies: these involve formulating functionalism or its causal claims in such a way that they appear not to generate the problem of metaphysically necessary effects. I argue against these indirect solutions, in each case concluding either that the problem still arises or that avoiding it requires the adoption of an unorthodox form of functionalism (itself a surprising result). In the final.. (shrink)

This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an additional effect. I further (...) distinguish between direct and indirect forms of downwards and upwards inferences. I then show how we can subsume canonical forms of inductive inference mentioned in the literature, such as inference to the best explanation, enumerative induction, and analogical inference, under this typology. Along the way, I explore connections with probability and confirmation, epistemic defeat, the relation between abduction and enumerative induction, the compatibility of IBE and Bayesianism, and theories of epistemic justification. (shrink)

The authors survey some debates about the nature and structure of physical theories and about the connections between our physical theories and naturalized metaphysics. The discussion is organized around an “ideal view” of physical theories and criticisms that can be raised against it. This view includes controversial commitments regarding the best analysis of physical modalities and intertheory relations. The authors consider the case in favor of taking laws as the primary modal notion, discussing objections related to alleged violations of the (...) laws, the apparent need to appeal to causality, and the status of probability. The “ideal view” includes a commitment that fundamental physical theories are explanatorily sufficient. The authors discuss several challenges to recovering the manifest image from fundamental physics, along with a distinct challenge to reductionism based on acknowledging the contributions of less fundamental theories in physical explanations. (shrink)

Causation is a macroscopic phenomenon. The temporal asymmetry displayed by causation must somehow emerge along with other asymmetric macroscopic phenomena like entropy increase and the arrow of radiation. I shall approach this issue by considering ‘causal inference’ techniques that allow causal relations to be inferred from sets of observed correlations. I shall show that these techniques are best explained by a reduction of causation to structures of equations with probabilistically independent exogenous terms. This exogenous probabilistic independence imposes a recursive order (...) on these equations and a consequent distinction between dependent and independent variables that lines up with the temporal asymmetry of causation. (shrink)

This paper continues and extensive exploration of the QG and TOE resulting from the PF interpretation of QM. Some highlights are an exploration of symmetries of the Standard Model, outlines of testable predictions of implications for QG, that this theory can simultaneously give an account of dark matter and dark energy, probabilities and statevector collapse vs. gravity, applications of the causal interaction tensor Cαβγδ(Fi, Fj). Note the first section mentions qualia but this is not a psychological theory this is an (...) ontic theory. (shrink)

Robert Holcot discusses the principle of causality in q. 3 of Determinationes, a text also preserved in q. 53 (ms. Cambridge, Pembroke College, 236) and q. 58 (ms. Oxford, Balliol College, 246) of Quodlibet I. In this text, Holcot maintains that the relation of causality is not evident. He gives two reasons: first, he calls into question the distinction between cause and effect – since the effect is not encompassed in the cause, it cannot necessarily follow from the (...) cause – and second, he invokes the possibility that God has to produce an effect entirely independent of any natural cause. The solution envisaged by Holcot understands the knowledge of causality as a case of probable knowledge (probabiliter). The relation of causality stems from observing the repetition of the relations between two res, so that when a res is present one can expect another to be present. The present article has two appendices: the first contains the version of the text conserved in mss Pembroke and Balliol, the second instead contains the version conserved in the ms. Düsseldorf, Universitäts-und Landesbibliothek, ms. F. 5 and in some early-modern printed editions. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. (...) Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

This paper explores the issue of the unification of three languages of physics, the geometric language of forces, geometric language of fields or 4-dimensional space-time, and probabilistic language of quantum mechanics. On the one hand, equations in each language may be derived from the Principle of Least Action (PLA). On the other hand, Feynman's path integral method could explain the physical meaning of PLA. The axioms of classical and relativistic mechanics can be considered as consequences of Feynman's formulation of quantum (...) mechanics. (shrink)

This paper calls for a re-appraisal of McGee's analysis of the semantics, logic and probabilities of indicative conditionals presented in his 1989 paper Conditional probabilities and compounds of conditionals. The probabilistic measures introduced by McGee are given a new axiomatisation built on the principle that the antecedent of a conditional is probabilistically independent of the conditional and a more transparent method of constructing such measures is provided. McGee's Dutch book argument is restructured to more clearly reveal that it introduces a (...) novel contribution to the epistemology of semantic indeterminacy, and shows that its more controversial implications are unavoidable if we want to maintain the Ramsey Test along with the standard laws of probability. Importantly, it is shown that the counterexamples that have been levelled at McGee's analysis|generating a rather wide consensus that it yields `unintuitive' or `wrong' probabilities for compounds |fail to strike at their intended target; for to honour the intuitions of the counterexamples one must either give up the Ramsey Test or the standard laws of probability. It will be argued that we need to give up neither if we take the counterexamples as further evidence that the indicative conditional sometimes allows for a non-epistemic `causal' interpretation alongside its usual epistemic interpretation. (shrink)

Probably the most distinctive feature of synthetic biology is its being “synthetic” in some sense or another. For some, synthesis plays a unique role in the production of knowledge that is most distinct from that played by analysis: it is claimed to deliver knowledge that would otherwise not be attained. In this contribution, my aim is to explore how synthetic biology delivers knowledge via synthesis, and to assess the extent to which this knowledge is distinctly synthetic. On the basis of (...) distinctions between knowledge-how and knowledge-why, and between syntheses that succeed and syntheses that fail, I argue that the contribution of synthesis to knowledge is best understood when syntheses are construed as experimental interventions that aim at probing causal relationships between properties of the entities that are combined through these syntheses and properties of their target products. The distinctiveness of synthetic biology in its quest for knowledge through synthesis stems from its ability to sample at will a space of empirical possibilities that is not only huge but also that has been so scarcely sampled by nature. (shrink)

By means of the Rollback Argument, this paper argues that metaphysically robust probabilities are incompatible with a kind of control which can ensure that free actions are not a matter of chance. Our main objection to those (typically agent-causal) theories which both attribute a kind of control to agents that eliminates the role of chance concerning free actions and ascribe probabilities to options of decisions is that metaphysically robust probabilities should be posited only if they can have a metaphysical explanatory (...) role but probabilities can explain anything only if chance has a role. First, we reconstruct the Rollback Argument. Second, we criticize the standard ways of reconciling non-chancy control with metaphysically robust probabilities. Finally, we respond to those worries that are related to the thought experiment of the Rollback Argument. (shrink)