The transferability problem—whether the results of an experiment will transfer to a treatment population—affects not only Randomized Controlled Trials but any type of study. The problem for any given type of study can also, potentially, be addressed to some degree through many different types of study. The transferability problem for a given RCT can be investigated further through another RCT, but the variables to use in the further experiment must be discovered. This suggests we could do better on the epistemological (...) problem of transferability by promoting, in the repeated process of formulating public health guidelines, feedback loops of information from the implementation setting back to researchers who are defining new studies. (shrink)

The current consensus view of causation in physics, as commonly held by scientists and philosophers, has several serious problems. It fails to provide an epistemology for the causal knowledge that it claims physics to possess; it is inapplicable in a prominent area of physics (classical thermodynamics); and it is difficult to reconcile with our everyday use of causal concepts and claims. In this dissertation, I use historical examples and philosophical arguments to show that the interventionist account of causation constitutes a (...) promising alternative for a “physically respectable” account of causation. The interventionist account explicates important parts of the experimental practice of physics and important aspects of the ways in which physical theory is used and applied. Moreover, the interventionist account succeeds where the consensus view of causation in physics fails. I argue that the interventionist account provides an epistemology of causal knowledge in physics that is rooted in experiment. On the interventionist view, there is a close link between experiment and the testing of causal claims. I give several examples of experiments from the early history of thermodynamics that scientists used in interventionist-type arguments. I also argue that interventionist claims made in the context of a physical theory can be epistemically justified by reference to the experimental interventions and observations that serve as evidence for the theory. I then show that the interventionist account of causation is well-suited to the patterns of reasoning that are intrinsic to thermodynamic theory. I argue that interventionist reasoning constitutes the structural foundation of thermodynamic theory, and that thermodynamic theory can provide clear answers to meaningful questions about whether or not a certain variable is a cause of another in a given context. Finally, I argue that the interventionist account offers the prospect of a unification of “physically respectable” causation and our everyday notion of causation. I conclude the dissertation by sketching an anti-foundationalist unification of causation, according to which causal reasoning occurs in the same manner in physics as it does in other branches of life and scientific research. (shrink)

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal nets and has been explicitly promoted by Glymour. In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause–effect relations can be (...) philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content. (shrink)

This paper defends an interventionist account of causation by construing this account as a contribution to methodology, rather than as a set of theses about the ontology or metaphysics of causation. It also uses the topic of causation to raise some more general issues about the relation between, on the one hand, methodology, and, on the other hand, ontology and metaphysics, as these are understood in contemporary philosophical discussion, particularly among so-called analytic metaphysicians. It concludes with the suggestion that issues (...) about the ontology of causation often can be fruitfully reconstrued as methodological proposals. (shrink)

There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.

Why do classic biostatistical studies, alleged to provide causal explanations of effects, often fail? This article argues that in statistics-relevant areas of biology—such as epidemiology, population biology, toxicology, and vector ecology—scientists often misunderstand epistemic constraints on use of the statistical-significance rule (SSR). As a result, biologists often make faulty causal inferences. The paper (1) provides several examples of faulty causal inferences that rely on tests of statistical significance; (2) uncovers the flawed theoretical assumptions, especially those related to randomization, that likely (...) contribute to flawed biostatistics; (3) re-assesses the three classic (SSR-warrant, avoiding-selection-bias, and avoiding-confounders) arguments for using SSR only with randomization; and (4) offers five new reasons for biologists to use SSR only with randomized experiments. (shrink)

Manipulablity theories of causation, according to which causes are to be regarded as handles or devices for manipulating effects, have considerable intuitive appeal and are popular among social scientists and statisticians. This article surveys several prominent versions of such theories advocated by philosophers, and the many difficulties they face. Philosophical statements of the manipulationist approach are generally reductionist in aspiration and assign a central role to human action. These contrast with recent discussions employing a broadly manipulationist framework for understanding causation, (...) such as those due to the computer scientist Judea Pearl and others, which are non-reductionist and rely instead on the notion of an intervention. This is simply an appropriately exogenous causal process; it has no essential connection with human action. This interventionist framework manages to avoid at least some of these difficulties faced by traditional philosophical versions of the manipulability theory and helps to clarify the content of causal claims. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three (...) key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

Through the introduction of his ‘common cause principle’ [The Direction of Time, 1956], Hans Reichenbach was the first to formulate a precise link relating causal claims to statements of probability. Despite some criticism, the principle has been hugely influential and successful—a pillar of scientific practice, as well as guiding our reasoning in everyday life. However, Bell’s theorem, taken in conjunction with quantum theory, challenges this principle in a fundamental sense at the microscopic level. For the same reason, the celebrated causal (...) model framework pioneered by Pearl, as well as by Spirtes, Glymour and Scheines, defies satisfactory causal explanations of quantum correlations—the role of the ‘causal Markov condition’ in this framework amounts to a generalisation of Reichenbach’s principle. Much effort has been devoted to the development of quantum causal models to overcome this challenge and to study whether a principled way of causal reasoning, albeit adjusted in its terms, can be maintained when it comes to quantum physics. A clarification of what quantum causal relations are supposed to be is also a much needed step in light of the hotly debated topic of causality in foundations of quantum theory, e.g. in the study of ‘indefinite causal structures’. This paper reports and discusses a framework of quantum causal models and argues that it is promising and interesting for two main reasons. First, it can be seen as a prime example of a recent research programme that took direct and fruitful inspiration from ideas first formulated by Reichenbach—it began by asking, and giving an answer to, how the common cause principle could be generalised appropriately to quantum theoretical terms. Second, the framework did not only facilitate generalisations of the core theorems of the classical framework of causal models, but, above all, it has a theorem at its center that justifies the ‘quantum causal Markov condition’ on the basis of what arguably is a natural definition of causal relations in quantum theory. This provides a conceptually clear grounding of the framework, as well as a proposal for the needed clarification of quantum causal relations in the foundations of quantum theory more generally. (shrink)

Interventionism is a theory of causation with a pragmatic goal: to define causal concepts that are useful for reasoning about how things could, in principle, be purposely manipulated. In its original presentation, Woodward’s interventionist definition of causation is relativized to an analyzed variable set. In Woodward, Woodward changes the definition of the most general interventionist notion of cause, contributing cause, so that it is no longer relativized to a variable set. This derelativization of interventionism has not gathered much attention, presumably (...) because it is seen as an unproblematic way to save the intuition that causal relations are objective features of the world. This paper first argues that this move has problematic consequences. Derelativization entails two concepts of unmediated causal relation that are not coextensional, but which nonetheless do not entail different conclusions about manipulability relations within any given variable set. This is in conflict with the pragmatic orientation at the core of interventionism. The paper then considers various approaches for resolving this tension but finds them all wanting. It is concluded that interventionist causation should not be derelativized in the first place. Various considerations are offered rendering that conclusion acceptable. (shrink)

It has been argued that supervenience generates unavoidable confounding problems for interventionist accounts of causation, to the point that we must choose between interventionism and supervenience. According to one solution, the dilemma can be defused by excluding non-causal determinants of an outcome as potential confounders. I argue that this solution undermines the methodological validity of causal tests. Moreover, we don’t have to choose between interventionism and supervenience in the first place. Some confounding problems are effectively circumvented by experimental designs routinely (...) employed in science. The remaining confounding issues concern the physical interpretation of variables and cannot be solved by choosing between interventionism and supervenience. (shrink)

Reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes (...) it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data. (shrink)

In this paper, I use interventionist causal models to identify some novel Newcomb problems, and subsequently use these problems to refine existing interventionist treatments of causal decision theory. The new Newcomb problems that make trouble for existing interventionist treatments involve so-called ‘exotic choice’—that is, decision-making contexts where the agent has evidence about the outcome of her choice. I argue that when choice is exotic, the interventionist can adequately capture causal decision-theoretic reasoning by introducing a new interventionist approach to updating on (...) exotic evidence. But I also argue that this new updating procedure is principled only if the interventionist trades in the typical interventionist conception of choice for an alternative Ramseyan conception. I end by arguing that the guide to exotic choice developed here may, despite its name, be useful in some everyday contexts. (shrink)

While ideal interventions are acknowledged by many as valuable tools for the analysis of causation, recent discussions have shown that, since there are no ideal interventions on upper-level phenomena that non-reductively supervene on their underlying mechanisms, interventions cannot—contrary to a popular opinion—ground an informative analysis of constitution. This has led some to abandon the project of analyzing constitution in interventionist terms. By contrast, this paper defines the notion of a horizontally surgical intervention, and argues that, when combined with some innocuous (...) metaphysical principles about the relation between upper and lower levels of mechanisms, that notion delivers a sufficient condition for constitution. This, in turn, strengthens the case for an interventionist analysis of constitution. (shrink)

Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected interventionist theory (...) of causation in philosophy. This monograph assesses five manipulationist or interventionist theories of causation, viewed as theories that purport to tell us what causation is by providing us with the meaning of causal claims. It is shown that they cannot do this, as the conditions on causation that they impose are too weak, mainly due to ineliminable circularities in their definitions of causal terms. It is then argued that a subset of Woodward’s theory can nevertheless contribute crucially to an explanation of the unique role that manipulation has in our acquisition of causal knowledge. This explanation differs from the common regularist explanation of the epistemic utility of manipulation and experiment, and it is taken to confirm several important manipulationist intuitions. However, the success of the explanation depends on (this subset of) interventionism not itself being understood as a theory of causation, but as a theory of intervention. (shrink)

In medicine and the social sciences, researchers often measure only a handful of variables simultaneously. The underlying assumption behind this methodology is that combining the results of dozens of smaller studies can, in principle, yield as much information as one large study, in which dozens of variables are measured simultaneously. Mayo-Wilson :864–874, 2011, Br J Philos Sci 65:213–249, 2013. https://doi.org/10.1093/bjps/axs030) shows that assumption is false when causal theories are inferred from observational data. This paper extends Mayo-Wilson’s results to cases in (...) which experimental data is available. I prove several new theorems that show that, as the number of variables under investigation grows, experiments do not improve, in the worst-case, one’s ability to identify the true causal model if one can measure only a few variables at a time. However, stronger statistical assumptions significantly aid causal discovery in piecemeal inquiry, even if such assumptions are unhelpful when all variables can be measured simultaneously. (shrink)

John Campbell has claimed that the interventionist account of causation must be amended if it is to be applied to causation in psychology. The problem, he argues, is that it follows from the so-called ‘surgical’ constraint that intervening on psychological states requires the suspension of the agent’s rational autonomy. In this paper, I argue that the problem Campbell identifies is in fact an instance of a wider problem for interventionism, extending beyond psychology, which I call the problem of ‘abrupt transitions’. (...) I then defend a solution to the problem, which replaces the surgical constraint with a weaker constraint on interventions that nevertheless does all the work the surgical constraint was designed to do. I conclude by exploring some interesting consequences of this weaker constraint for causation in psychology. (shrink)

In this paper we show that the application of Occam’s razor to the theory of causal Bayes nets gives us a neat definition of direct causation. In particular we show that Occam’s razor implies Woodward’s (2003) definition of direct causation, provided suitable intervention variables exist and the causal Markov condition (CMC) is satisfied. We also show how Occam’s razor can account for direct causal relationships Woodward style when only stochastic intervention variables are available.

In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the (...) phenomena the mechanism brings about. Finally, I sketch an alternative approach taking (i) and (ii) into account. (shrink)

The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention (...) variables only if they also answer the questions that can be answered by interventionist techniques—which are thus rendered dispensable. The paper concludes by suggesting a way of identifying intervention variables that allows for exploiting the whole inferential potential of interventionist techniques. (shrink)

Mechanisms are often characterized as causal structures and the interventionist account of causation is then used to characterize what it is to be a causal structure. The associated modularity constraint on causal structures has evoked criticism against using the theory as an account of mechanisms, since many mechanisms seem to violate modularity. This paper answers to this criticism by making a distinction between a causal system and a causal structure. It makes sense to ask what the modularity properties of a (...) given causal structure are, but not whether a causal system is modular tout court. The counter-examples to the interventionist account are systems in which a particular structure is modular in variables, but not in parameters. A failure of parameter-modularity does not by itself threaten the interventionist interpretation of the structure and the possibility of causally explaining with that structure, but it does mean that knowledge of the structure is not sufficient to constitutively explain system-level properties of the embedding system. (shrink)

This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...) foundations of causal discovery procedures, and how they can be improved. (shrink)

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same time (...) changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.

In this paper I present a limitation to what may be called strictly-interventionistic causal-model semantic theories for subjunctive conditionals. And I offer a line of response to Briggs’ counterexample to Modus Ponens—given within a strictly-interventionistic framework—for the subjunctive conditional. The paper also contains some discussion of backtracking counterfactuals and backtracking interpretations. The limitation inherent to strict interventionism is brought out via a class of counterexamples. A causal-model semantics is strictly interventionistic just in case the procedure it gives for evaluating a (...) subjunctive conditional requires making the values of the variables implicated in the antecedent independent from the values of the parents of these antecedent variables. Most causal-model semantic theories that have gained attention are strictly interventionistic. (shrink)

The experimental interventions that provide evidence of causal relations are notably similar to those that provide evidence of constitutive relevance relations. In the first two sections, I show that this similarity creates a tension: there is an inconsistent triad between Woodward’s popular interventionist theory of causation, Craver’s mutual manipulability account of constitutive relevance in mechanisms, and a variety of arguments for the incoherence of inter-level causation. I argue for an interpretation of the views in which the tension is merely apparent. (...) I propose to explain inter-level relations without inter-level causation by appealing to the notion of fat-handed interventions, and an argument against inter-level causation which dissolves the problem. (shrink)

Philosophy and statistics have studied two causal species, deterministic and probabilistic. There's a third species, however, hitherto unanalysed: underdeterministic causal phenomena, which are non-deterministic yet non-probabilistic. Here, I formulate a framework for modelling them. -/- Consider a simple case. If I go out, I may stumble into you but also may miss you. If I don’t go out, we won't meet. I go out. We meet. My going out is a cause of our encounter even if there was no determinate (...) probability of us meeting conditional on my going out. The cause is neither deterministic (it didn't necessitate the effect) nor probabilistic (the relevant conditional probabilities are undefined). Rather, it's underdeterministic: it raises the modal status of the effect from causally impossible to possible. -/- Here, I won't offer a theory of such token causes but develop the prerequisite for any such theory: the underdeterministic framework. The framework is like the deterministic structural-equations framework but with one consequential difference---an equation can return multiple values. This change allows me to define causal possibility and necessity, and corresponding notions of interventionist might- and would-counterfactuals. I also define conditional independence, which obeys the graphoid axioms, and prove that underdeterministic models satisfy the causal Markov condition. The framework can causally model situations that other frameworks cannot: decision-making under bounded uncertainty, games with multiple equilibria, infinite fair lotteries, and any other non-deterministic situation where indeterminacies are essentially non-probabilistic, or where we have a reason not to use probabilities. (shrink)

This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.

The interventionist account of causation has been largely dismissed as a serious candidate for application in physics. This dismissal is related to the problematic assumption that physical causation is entirely a matter of dynamical evolution. In this article, I offer a fresh look at the interventionist account of causation and its applicability to thermodynamics. I argue that the interventionist account of causation is the account of causation that most appropriately characterizes the theoretical structure and phenomenal behavior of thermodynamics.

Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code'. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of graphs for expressing our theory of biological codes and discuss its (...) utility in understanding biological systems. *Received May 2007; revised May 2008. †To contact the authors, please write to: Department of Philosophy, Baker Hall 135, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] or [email protected]. (shrink)

We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models. We then draw a parallel between the latent variable models and Bayesian networks with hidden nodes. This allows us to clarify the use of interventions for dealing with unidentified statistical models. We end by (...) discussing the philosophical and methodological import of our result. (shrink)

I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making things happen: A theory of causal explanation. Making things happen gives an elegant interventionist theory for understanding explanation and causation. The new contribution relies on that theory and further makes a big step towards empirical inference of causal relations from non-experimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement (...) Woodward's contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders, and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions. (shrink)

Kerr and Godfrey-Smith argue that two mathematically equivalent, alternative formal representations drawn from population genetics, the contextualist and collectivist formalisms, may be equally good for quantifying the dynamics of some natural systems, despite important differences between the formalisms. I draw on constraints on causal representation from Woodward (Making things happen, Oxford University Press, New York, 2003) and Eberhardt and Scheines (Philos Sci 74(5):981–995, 2006) to argue that one or the other formalism will be superior for arbitrary natural systems in which (...) individuals form different types of groups. (shrink)

Reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes (...) it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data. (shrink)