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Switch to: References
Citations of:
Causality: Models, Reasoning and Inference
Tijdschrift Voor Filosofie 64 (1):201-202 (2000)
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Reichenbach's 'principle of the common cause' is a foundational assumption of some important recent contributions to quantitative social science methodology but no similar principle appears in econometrics. Reiss (2005) has argued that the principle is necessary for instrumental variables methods in econometrics, and Pearl (2009) builds a framework using it that he proposes as a means of resolving an important methodological dispute among econometricians. We aim to show, through analysis of the main problem instrumental variables methods are used to resolve, (...) |
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Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...) |
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Abductive reasoning is central to reconstructing the past in the geosciences. This paper outlines the nature of the abductive method and restates it in Bayesian terms. Evidence plays a key role in this working method and, in particular, traces of the past are important in this explanatory framework. Traces, whether singularly or as groups, are interpreted within the context of the event for which they have evidential claims. Traces are not considered as independent entities but rather as inter-related pieces of (...) |
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Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use (...) |
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There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical learning (...) |
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This chapter provides an introduction to the study of the philosophical notions of mechanisms and causality in biology and economics. This chapter sets the stage for this volume, Mechanism and Causality in Biology and Economics, in three ways. First, it gives a broad review of the recent changes and current state of the study of mechanisms and causality in the philosophy of science. Second, consistent with a recent trend in the philosophy of science to focus on scientific practices, it in (...) |
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Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...) |
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This paper aims to provide Humean metaphysics for the interventionist theory of causation. This is done by appealing to the hierarchical picture of causal relations as being realized by mechanisms, which in turn are identified with lower-level causal structures. The modal content of invariances at the lowest level of this hierarchy, at which mechanisms are reduced to strict natural laws, is then explained in terms of projectivism based on the best-system view of laws. |
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The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special (...) |
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I argue that a phenotypic trait can be an adaptation to a particular environmental condition, as against others, only if the environmental condition and the phenotype interactively cause fitness. Models of interactive environmental causes of fitness generally require that environments be individuated by explicit representation rather than by measures of environmental quality, although the latter understanding of ‘environment’ is more prominent in the philosophy of biology. Hence, talk of adaptations to some but not other environmental conditions relies on conceptions of (...) |
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A measure of coherence is said to be reliability conducive if and only if a higher degree of coherence (as measured) results in a higher likelihood that the witnesses are reliable. Recently, it has been proved that several coherence measures proposed in the literature are reliability conducive in a restricted scenario (Olsson and Schubert 2007, Synthese 157:297–308). My aim is to investigate which coherence measures turn out to be reliability conducive in the more general scenario where it is any finite (...) |
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Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures (...) |
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There is a tradition, tracing back to Kant, of recasting metaphysical questions as questions about the utility of a conceptual scheme, linguistic framework, or methodological rule for achieving some particular end. Following in this tradition, I propose a ‘means-ends metaphysics ’, in which one rigorously demonstrates the suitability of some conceptual framework for achieving a specified goal. I illustrate this approach using a debate about the nature of events. Specifically, the question is whether the time at which an event occurs (...) |
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Let us by ‘first-order beliefs’ mean beliefs about the world, such as the belief that it will rain tomorrow, and by ‘second-order beliefs’ let us mean beliefs about the reliability of first-order, belief-forming processes. In formal epistemology, coherence has been studied, with much ingenuity and precision, for sets of first-order beliefs. However, to the best of our knowledge, sets including second-order beliefs have not yet received serious attention in that literature. In informal epistemology, by contrast, sets of the latter kind (...) |
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Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent to their consequents. (...) |
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We critically examine Denis Walsh’s latest attack on the causalist view of fitness. Relying on Judea Pearl’s Sure-Thing Principle and geneticist John Gillespie’s model for fitness, Walsh has argued that the causal interpretation of fitness results in a reductio. We show that his conclusion only follows from misuse of the models, that is, (1) the disregard of the real biological bearing of the population-size parameter in Gillespie’s model and (2) the confusion of the distinction between ordinary probability and Pearl’s causal (...) |
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Modeling a complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, a problem or some theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called (...) |
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cal practice: the enterprise of specifying information about the world for use in computer systems. Knowledge representation as a field also encompasses conceptual results that call practitioners’ attention to important truths about the world, mathematical results that allow practitioners to make these truths precise, and computational results that put these truths to work. This chapter surveys this practice and its results, as it applies to the interpretation of natural language utterances in implemented natural language processing systems. For a broader perspective (...) |
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A measure of coherence is said to be reliability conducive if and only if a higher degree of coherence (as measured) among testimonies implies a higher probability that the witnesses are reliable. Recently, it has been proved that several coherence measures proposed in the literature are reliability conducive in scenarios of equivalent testimonies (Olsson and Schubert 2007; Schubert, to appear). My aim is to investigate which coherence measures turn out to be reliability conducive in the more general scenario where the (...) |
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This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: an epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that (...) |
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This article proposes a unified framework for understanding creative problem solving, namely, the explicit–implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit–implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most (...) |
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Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction (...) |
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This article raises two difficulties that certain approaches to causation have with would‐cause counterfactuals. First, there is a problem with David Lewis’s semantics of counterfactuals when we ‘suppose in’ some positive event of a certain kind. And, second, there is a problem with embedded counterfactuals. I show that causal‐modeling approaches do not have these problems. †To contact the author, please write to: Philosophy, University of Queensland, Brisbane, Queensland 4072, Australia; e‐mail: [email protected]. |
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An alleged counterexample to causal decision theory, put forward by Andy Egan, is studied in some detail. It is argued that Egan rejects the evaluation of causal decision theory on the basis of a description of the decision situation that is different from—indeed inconsistent with—the description on which causal decision theory makes its evaluation. So the example is not a counterexample to causal decision theory. Nevertheless, the example shows that causal decision theory can recommend unratifiable acts which presents a problem (...) |
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Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) |
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This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of (...) |
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We present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. (...) |
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The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...) |
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The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. (...) |
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Nancy Cartwright offers an account of causal powers, and argues that it explains some important general features of scientific method. Patricia Cheng argues that this theory is superior as a psychological theory of learning to standard models of conditioning. I extend and develop the theory, and argue that it provides the best explanation of a number of problem cases for philosophical theories of causation, including preemption, overdetermination and puzzles about transitivity. Hitchcock and Halpern & Pearl on ‘actual causes’ Problems and (...) |
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We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficultiesn in the traditional account. |
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We further develop the mathematical theory of causal interventions, extending earlier results of Korb, Twardy, Handfield, & Oppy, (2005) and Spirtes, Glymour, Scheines (2000). Some of the skepticism surrounding causal discovery has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (cf. Cartwright, (2001). Our results show that experimental data, together with some plausible assumptions, can reduce the space of viable explanatory (...) |
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Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST - Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in the epistemological (...) |
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This paper is on the problem of profligate omissions. The problem is that counterfactual definitions of causation identify as a cause anything that could have prevented an effect but that did not actually occur, which is a highly counterintuitive result. Many solutions of this problem appeal to normative, epistemic, pragmatic, or metaphysical considerations. These existing solutions are in some sense substantive. In contrast, this paper concentrates on the semantics of counterfactuals. I propose to replace Strong Centering with Weak Centering. This (...) |
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Intersecting systems of inequality are remarkably resistant to change. Many universities, however, seek National Science Foundation Institutional Transformation awards to change processes, procedures, and cultures to make science, technology, engineering, and mathematics departments more inclusive. In this article we describe a case study with observations for eight years of before, five during, and seven after intensive efforts to increase women through reducing barriers and increasing access to women. Finally, we reflect on flawed assumptions built into the proposal, the slow and (...) |
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Debates concerning the units and levels of selection have persisted for over fifty years. One major question in this literature is whether units and levels of selection are genuine, in the sense that they are objective features of the world, or merely reflect the interests and goals of an observer. Scientists and philosophers have proposed a range of answers to this question. This Element introduces this literature and proposes a novel contribution. It defends a realist stance and offers a way (...) |
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Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed. We show that, under certain monotonicity assumption that is empirically verifiable, adjusting for the proxy produces a measure of the effect that is between the unadjusted and the true measures. |
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Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side (...) |
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Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has (...) |
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The principle of the common cause demands that every pair of causally independent but statistically correlated events should be the effect of a common cause. This demand is often supplemented with the requirement that said cause should screen-off the two events from each other. This paper introduces a new probabilistic model for common causes, which generalises this requirement to include sets of distinct but non-disjoint causes. It is demonstrated that the model hereby proposed satisfies the explanatory function generally attributed to (...) |
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An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also (...) |
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In this dissertation, I develop and defend a model of causation that allows for dualist mental causation in worlds where the physical domain is physically complete. In Part I, I present the dualist ontology that will be assumed throughout the thesis and identify two challenges for models of mental causation within such an ontology: the exclusion worry and the common cause worry. I also argue that a proper response to these challenges requires a thoroughly lightweight account of causation, i.e. an (...) |
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