Natural conditions are provided that are sufficient to ensure that causality as defined by approaches that use counterfactual dependence and structural equations will be transitive.

The article deals with one particular problem created by the counterfactual analysis of causality à la Lewis, namely the context-sensitivity problem or, as I prefer to call it, the background condition problem. It appears that Lewis’ counterfactual definition of causality cannot distinguish between proper causes and mere causal conditions – i.e. factors necessary for the effect to occur, but commonly not seen as causally efficacious. The proposal is put forward to amend the Lewis definition with a condition, based (...) on the notion of cotenability, which would eliminate the problem. It is shown that the corrected definition of causality leads to the transitivity of the causal relation. Possible objections to the proposed solution, involving the assumption of indeterminism and the preemption cases, are given a thorough consideration. (shrink)

Unlike any other monograph on legal liability, Michael S. Moore’s book CAUSATION AND RESPONSIBILITY contains a well-informed and in-depth discussion of the metaphysics of causation. Moore does not share the widespread view that legal scholars should not enter into metaphysical debates about causation. He shows respect for the subtleties of philosophical debates on causal relata, identity conditions for events, the ontological distinctions between events, states of affairs, facts and tropes, and the counterfactual analysis of event causation, and he considers all (...) these issues relevant to law. In this contribution, I defend an amended version of the COUNTERFACTUAL theory of event causation both against Moore’s criticism and against some traits of Lewis’s version of the theory. On a number of counts, I simply defend Lewis against Moore’s misdirected criticism. Moore’s unreasonable demand that all the commonplaces about causation have to FOLLOW from the counterfactual analysis rests on his mistaken claim that the counterfactual theory IDENTIFIES causation with counterfactual dependence. In other respects, I part company with Lewis in order to highlight some underrated strengths of the counterfactual approach. The common denominator of my revisions is that they make the counterfactual theory unambiguously singularist. The suggested revisions comprise: performing the counterfactual analysis with Davidsonian rather than with Lewisian events, taking seriously the ex post-character of singular causal statements, making explicit the indexical ceteris paribus clause that fixes the actual circumstances of the causing event, abandoning the alleged transitivity of causation, and, last but not least, doing everything in the correct order. The correct order is to start with bedrock intuitions about the nature of causality rather than with a general possible world semantics, and then to turn these intuitions into constraints for the class of counterfactuals that a counterfactual analysis of causation has to consider. (shrink)

We study abductive, causal, and non-causal conditionals in indicative and counterfactual formulations using probabilistic truth table tasks under incomplete probabilistic knowledge (N = 80). We frame the task as a probability-logical inference problem. The most frequently observed response type across all conditions was a class of conditional event interpretations of conditionals; it was followed by conjunction interpretations. An interesting minority of participants neglected some of the relevant imprecision involved in the premises when inferring lower or upper probability bounds on the (...) target conditional/counterfactual ("halfway responses"). We discuss the results in the light of coherence-based probability logic and the new paradigm psychology of reasoning. (shrink)

In a recent paper Causal Asymmetry, Douglas Ehring has proposed an intriguing solution to the vexing problem of causal asymmetry. The aim of this paper is to show that his theory is not satisfactory. Moreover, the examples that I use in showing the defect of Ehring's theory also indicate that the counterfactual analysis of causation has a problem that cannot be remedied by Marshall Swain's suggested refinement of the counterfactual analysis of causation in Causation and Distinct Events.

A theory of necessary and sufficient conditions is presented, as well as a theory of necessary and sufficient causes and effects, viewed as a particular case of the former. Ambiguities of the terms 'condition' and 'necessary condition' are explored, and a neutral meaning for 'condition' is favoured. The relation between necessary and sufficient conditions and implicative conditionals (including counterfactuals) is also discussed. Two problems of counterfactual theories of causal explanation are indicated, concerning (i) how to account for causes that (...) are not necessary and (ii) how to interpret the counterfactual from the negation of the effect to the negation of the cause. Application of the theory of necessary and sufficient conditions to Euler’s explanation of the Königsberg bridge problem is discussed. The theory of necessary and sufficient causes and effects is then applied to the flagpole shadow explanation and to two biological explanations, one involving a sufficient but not necessary cause and the other a necessary but not sufficient cause. Causes that are neither sufficient nor necessary are also recognized. Finally, a probabilistic approach to causes that are either not sufficient or not necessary is explored, including its application to examples of such cases. (shrink)

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 (...) morals 2.1 Puzzles about prevention 2.2 Counterfactuals Causal powers 3.1 Generative causal power 3.2 Preventative causal power Net and component powers ‘Actual’ or ‘successful’ causes Solutions to puzzle cases Conclusion. (shrink)

This paper deals with the truth conditions of conditional sentences. It focuses on a particular class of problematic examples for semantic theories for these sentences. I will argue that the examples show the need to refer to dynamic, in particular causal laws in an approach to their truth conditions. More particularly, I will claim that we need a causal notion of consequence. The proposal subsequently made uses a representation of causal dependencies as proposed in Pearl to formalize a causal notion (...) of consequence. This notion inserted in premise semantics for counterfactuals in the style of Veltman and Kratzer will provide a new interpretation rule for conditionals. I will illustrate how this approach overcomes problems of previous proposals and end with some remarks on remaining questions. (shrink)

One task of historians is to construct causal ascription of singular historicals between eminent historical events. For instance, the controversy resulting from the confusing butterfly ballot of Florida’s year 2000 presidential election cost Gore his presidency. However, to research into these matters is inevitably to appeal to counterfactual deliberation in an epistemic fashion because the past is fixed. One standard idea is Max Weber’s, Weber causation: “f was a cause of φ” is assertable iff “¬f □→ ¬φ” is assertable. Reiss (...) gives an exceptionally good analysis of this topic and outlines historians’ reasoning, claiming that backtracking analyses of counterfactual conditionals employed in historical thought experiments is the signature of historical study of causal ascription of singular historicals. Nevertheless, he concludes that it is very difficult to reach an uncontroversial ascription for this sort in most cases. For this reason, he proposes to find difference-making relations that will suffice. The objective of this paper is to provide a more fine-grained, intervention-based, backtracking analysis of counterfactual conditionals upon which a more satisfactory account of causal ascription of singular historicals can be given. Reiss’ account of difference-making relation will be shown to be unsatisfactory. Moreover, a formal ground of the epistemology of historical thought experiments can be given, along with the constraints of this account resultant from the semantic features of non-transitivity and strong centering of counterfactual conditionals. Finally, some epistemological points of causal ascription of singular historicals and historical thought experiments will be given. (shrink)

In this dissertation, I propose a reductive account of causation. This account may be stated as follows: -/- Causation:c is a cause of e within a possibility horizon H iff a) c is process-connected to e, and b) e security-depends on c within H. -/- More precisely, my suggestion is that there are two kinds of causal relata: instantaneous events (defined in Chapter 4) and possibility horizons (defined in Chapter 5). Causation is a ternary relation between two actual instantaneous events (...) – the cause c and the effect e – and a possibility horizon H. -/- I argue that causation has a dual nature: on the one hand, a cause must be connected to its effect via a genuine process; on the other hand, a cause must make a difference to its effect. The first condition – namely, the condition of process-connection (defined in Chapter 6) – captures the sense in which a cause must be connected to its effect via a genuine process. This condition allows my account to separate causation from mere correlation, distinguish genuine causes from preempted backups, and capture how a cause must be at the right level of detail relative to its effect (Chapter 7). -/- The second condition – namely, the condition of security-dependence (defined in Chapter 8) – captures the sense in which a cause must make a difference to its effect. This condition allows my account to yield intuitively correct verdicts on the counterexamples to the transitivity and intrinsicness of causation, resolve the problem of profligate omissions, accommodate structurally isomorphic but causally different cases, and handle contrastive causal claims (Chapter 9 and 10). -/- Finally, my proposed account of causation logically entails restricted versions of three important principles of causal reasoning concerning the sufficiency of counterfactual dependence for causation, and the transitivity and intrinsicness of causation (Chapter 11). (shrink)

Conditionals are basic for human reasoning. In our paper, we present two experiments, which for the first time systematically compare how people reason about indicative conditionals (Experiment 1) and counterfactual conditionals (Experiment 2) in causal and non-causal task settings (N = 80). The main result of both experiments is that conditional probability is the dominant response pattern and thus a key ingredient for modeling causal, indicative, and counterfactual conditionals. In the paper, we will give an overview of the main experimental (...) results and discuss their relevance for understanding how people reason about conditionals. (shrink)

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of (...) the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data. (shrink)

The aim of this paper is to develop an adequate version of the D-N theory of explanation for particular events and to show how the resulting D-N model can be used as a tool in articulating a regularity theory of causation and an analysis of the truth conditions for counterfactual conditionals. Starting with a basic model that is largely the product of other workers in this field, two new restrictions are formulated in order to construct a version of D-N explanation (...) that does not yield the counterintuitive results that have plagued all earlier versions. An additional condition is then developed that is indispensable for utilizing the D-N model as a tool in formulating a regularity theory of causation. Finally, it is shown how a suitable model of potential D-N explanation facilitates the formulation of a theory of counterfactual conditionals. (shrink)

This paper is about two controversial inference-patterns involving counterfactual or subjunctive conditionals. Given a plausible assumption about the truth-conditions of counterfactuals, it is shown that one can't go wrong in applying hypothetical syllogism (i.e., transitivity) so long as the set of worlds relevant for the conclusion is a subset of the sets of worlds relevant for the premises. It is also shown that one can't go wrong in applying antecedent strengthening so long as the set of worlds relevant for (...) the conclusion is a subset of that for the premise. These results are then adapted to Lewis's theory of counterfactuals. (shrink)

Rubin and Pearl offered approaches to causal effect estimation and Lewis and Pearl offered theories of counterfactual conditionals. Arguments offered by Pearl and his collaborators support a weak form of equivalence such that notation from the rival theory can be re-purposed to express Pearl’s theory in a way that is equivalent to Pearl’s theory expressed in its native notation. Nonetheless, the many fundamental differences between the theories rule out any stronger form of equivalence. A renewed emphasis on comparative research can (...) help to guide applications, further develop each theory, and better understand their relative strengths and weaknesses. (shrink)

When is a cause of a cause of an effect also a cause of that effect? The right answer is either Sometimes or Always . In favour of Always , transitivity is considered by some to be necessary for distinguishing causes from redundant non-causal events. Moreover transitivity may be motivated by an interest in an unselective notion of causation, untroubled by principles of invidious discrimination. And causal relations appear to add up like transitive relations, so that the obtaining of the (...) overarching relation is not independent of the obtaining of the intermediaries. On the other hand, in favour of Sometimes , often we seem not to treat events that are very spatiotemporally remote from an effect as its causes, even when connected to the effect in question by a chain of counterfactual or chance-raising dependence. Moreover cases of double prevention provide counterexamples to causal transitivity even over short chains. According to the argument of this paper, causation is non-transitive. Transitizing causation provides no viable account of causal redundancy. An unselective approach to causation may motivate resisting the distance counterexamples to transitivity, but it does not help with double prevention, and even makes it more intractable. The strongest point in favour of transitivity is the adding up of causal relations, and this is the point that extant non-transitizing analyses have not adequately addressed. I propose a necessary condition on causation that explains the adding up phenomenon. In doing so it also provides a unifying explanation of distance and double prevention counterexamples to transitivity. (shrink)

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. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)

Hume's "other words" which follow his first definition of causality in the "enquiry" are standardly read as giving us a counterfactual conditional. I argue that a more accurate reading reveals them to constitute a factual conditional, One reflecting a temporal restriction implicit in the first definition. The other words, So understood, Tell us merely that a component of the relation defined in the first definition is symmetrical.

We construct a causal-modeling semantics for both indicative and counterfactual conditionals. As regards counterfactuals, we adopt the orthodox view that a counterfactual conditional is true in a causal model M just in case its consequent is true in the submodel M∗, generated by intervening in M, in which its antecedent is true. We supplement the orthodox semantics by introducing a new manipulation called extrapolation. We argue that an indicative conditional is true in a causal model M just in case (...) its con- sequent is true in certain submodels M∗, generated by extrapolating M, in which its antecedent is true. We show that the proposed semantics can account for some important minimal pairs nicely and naturally. We also prove a theorem showing under what conditions intervention and extrapolation will yield the same result, and thus explain how counterfactual and indicative conditionals would behave in a causal-modeling semantics. (shrink)

Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: (...) prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions. (shrink)

Dispositional essentialists are typically committed to two claims: that properties are individuated by their causal role (‘causal structuralism’), and that natural necessity is to be explained by appeal to these causal roles (‘dispositional actualism’). I argue that these two claims cannot be simultaneously maintained; and that the correct response is to deny dispositional actualism. Causal structuralism remains an attractive position, but doesn’t in fact provide much support for dispositional essentialism.

Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This paper is (...) concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar (...) principles of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This paper is (...) concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

Counterfactuals are inherently ambiguous in the sense that the same counterfactual may be true under one mode of counterfactualization but false under the other. Many have regarded the ambiguity of counterfactuals as consisting in the distinction between forward-tracking and backtracking counterfactuals. This is incorrect since the ambiguity persists even in cases not involving backtracking counterfactualization. In this paper, I argue that causal modeling semantics has the resources enough for accounting for the ambiguity of counterfactuals. Specifically, we (...) need to distinguish two types of causal manipulation, which I call “intervention” and “extrapolation” respectively. To intervene in a causal model M is to change M’s structural equations in some specific ways, while to extrapolate M is to change the value assignment of M’s variables in some specific ways. I argue that intervention and extrapolation offer a natural explanation for the ambiguity of counterfactuals. (shrink)

If the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble (...) for philosophical theories which rely upon counterfactual conditionals—like, for instance, causal decision theory. Here, I outline a semantics for counterfactual conditionals which allows us to accept both (1) and (2). And I discuss how this semantics deals with objections to causal decision theory from Arif Ahmed. (shrink)

How are causal judgements such as 'The ice on the road caused the traffic accident' connected with counterfactual judgements such as 'If there had not been any ice on the road, the traffic accident would not have happened'? This volume throws new light on this question by uniting, for the first time, psychological and philosophical approaches to causation and counterfactuals. Traditionally, philosophers have primarily been interested in connections between causal and counterfactual claims on the level of meaning or truth-conditions. (...) More recently, however, they have also increasingly turned their attention to psychological connections between causal and counterfactual understanding or reasoning. At the same time, there has been a surge in interest in empirical work on causal and counterfactual cognition amongst developmental, cognitive, and social psychologists--much of it inspired by work in philosophy. In this volume, twelve original contributions from leading philosophers and psychologists explore in detail what bearing empirical findings might have on philosophical concerns about counterfactuals and causation, and how, in turn, work in philosophy might help clarify the issues at stake in empirical work on the cognitive underpinnings of, and relationships between, causal and counterfactual thought. (shrink)

Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these two (...) semantic treatments and argue that my (bifurcated) semantics is theoretically more promising than Hiddleston’s (unified) semantics. (shrink)

Using Jim Woodward's Counterfactual Dependency account as an example, I argue that causal claims about indeterministic systems cannot be satisfactorily analysed as including counterfactual conditionals among their truth conditions because the counterfactuals such accounts must appeal to need not have truth values. Where this happens, counterfactual analyses transform true causal claims into expressions which are not true.

I develop an account of counterfactual conditionals using “causal models”, and argue that this account is preferable to the currently standard account in terms of “similarity of possible worlds” due to David Lewis and Robert Stalnaker. I diagnose the attraction of counterfactual theories of causation, and argue that it is illusory.

We define a formal semantics of conditionals based on _normatively ideal worlds_. Such worlds are described informally by Armgardt (Gabbay D, Magnani L, Park W, Pietarinen A-V (eds) Natural arguments: a tribute to john woods, College Publications, London, pp 699–708, 2018) to address well-known problems of the counterfactual approach to causation. Drawing on Armgardt’s proposal, we use iterated conditionals in order to analyse causal relations in scenarios of multi-agent interaction. This results in a refined counterfactual approach to causal responsibility in (...) legal contexts, which solves overdetermination problems in an intuitively accessible manner. (shrink)

Counterfactuals are inherently ambiguous in the sense that the same counterfactual may be true under one mode of counterfactualization but false under the other. Many have regarded the ambiguity of counterfactuals as consisting in the distinction between forward-tracking and backtracking counterfactuals. This is incorrect since the ambiguity persists even in cases not involving backtracking counterfactualization. In this paper, I argue that causal modeling semantics has the resources enough for accounting for the ambiguity of counterfactuals. Specifically, we (...) need to distinguish two types of causal manipulation, which I call “intervention” and “extrapolation” respectively. To intervene in a causal model M is to change M’s structural equations in some specific ways, while to extrapolate M is to change the value assignment of M’s variables in some specific ways. I argue that intervention and extrapolation offer a natural explanation for the ambiguity of counterfactuals. (shrink)

After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.

Some have argued that no analysis of counterfactual conditionals can succeed without appealing to causal notions. Such authors claim that, in determining what would transpire had some events gone differently, we hold fixed everything that is causally independent from those events. Call this view Causal Independence. Some have argued that we need Causal Independence to accommodate intuitive judgments about certain kinds of counterfactuals in indeterministic worlds. The aim of this paper is to show that, contra these authors, Causal Independence (...) systematically delivers counterintuitive results for a certain subset of such counterfactuals-namely, those involving causation by double prevention. I conclude that intuitions about such counterfactuals do not motivate Causal Independence, at least in any form in which it has thus far been articulated. However, I suggest that a refined Causal Independence thesis that presupposes a kind of causal pluralism might be able to accommodate these intuitions, though such a refined version of Causal Independence may not conflict with reductive analyses of causal notions that appeal to counterfactuals after all. (shrink)

The 2021 Nobel Prize in Economics recognized an epistemology of causal inference based on the Rubin causal model (Rubin 1974), which merits broader attention in philosophy. This model, in fact, presupposes a logical principle of counterfactuals, Conditional Excluded Middle (CEM), the locus of a pivotal debate between Stalnaker (1968) and Lewis (1973) on the semantics of counterfactuals. Proponents of CEM should recognize that this connection points to a new argument for CEM---a Quine-Putnam indispensability argument grounded in the Nobel-winning (...) applications of the Rubin model in health and social sciences. To advance the dialectic, I challenge this argument with an updated Rubin causal model that retains its successes while dispensing with CEM. This novel approach combines the strengths of the Rubin causal model and a causal model familiar in philosophy, the causal Bayes net. The takeaway: deductive logic and inductive inference, often studied in isolation, are deeply interconnected. (shrink)

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. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)

In the artificial intelligence literature a promising approach to counterfactual reasoning is to interpret counterfactual conditionals based on causal models. Different logics of such causal counterfactuals have been developed with respect to different classes of causal models. In this paper I characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals. I then develop a system sound and complete with respect to this class. The resulting (...) logic is the weakest logic of causal counterfactuals that respects Lewis’s principles, sits in between the logic developed by Galles and Pearl and the logic developed by Halpern, and stands to Galles and Pearl’s logic in the same fashion as Lewis’s stands to Stalnaker’s. (shrink)

[p.225] Introduction (i) Although the following essay attempts to deal in a connected way with a number of connected conceptual tangles, it is by no means monolithic in design. It divides roughly in two, with the first half (Parts I and II) devoted to certain puzzles which have their source in a misunderstanding of the more specific structure of the language in which we describe and explain natural phenomena; while the second half (Parts III and IV) attempts to resolve the (...) more sweeping controversy over the nature of the connection between 'cause' and 'effect,' or, in modem dress, the logical status of 'lawlike statements.' (ii) The essay begins with a case analysis of a puzzle, taken from recent philosophical literature, relating to the analysis of counterfactual conditionals, statements of the form "If that lump of salt had been put in water, it would have dissolved." The diagnosis of this puzzle, which occupies the whole of Part I, shows it to rest on a misunderstanding of the conceptual framework in terms of which we speak of what things do when acted upon in certain ways in certain kinds of circumstance. Although the puzzle is initially posed in terms of examples taken from everyday life, the logical features of these examples which, misunderstood, generate the puzzle, are to be found in even the more theoretical levels of the language of science, and the puzzle is as much at home in the one place as in the other. For the framework in which things of various kinds (e.g. matches, white rats) behave ('respond') in various ways (catch fire, leap at a door) when acted upon ('submitted to such and such stimuli') under given conditions (presence of oxygen, 24 hours of food deprivation) is far more basic than the distinctions between metrical and non-metrical concepts, molar and micro-things, [p.226] observable and unobservable.. (shrink)

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)

Philosophers have long analyzed the truth-condition of counterfactual conditionals in terms of the possible-worlds semantics advanced by Lewis [13] and Stalnaker [23]. In this paper, I argue that, from the perspective of philosophical semantics, the causal modeling semantics proposed by Pearl [17] and others (e.g., Briggs [3]) is more plausible than the Lewis-Stalnaker possible-worlds semantics. I offer two reasons. First, the possible-worlds semantics has suffered from a specific type of counterexamples. While the causal modeling semantics can handle such examples with (...) ease, the only way for the possible-worlds semantics to do so seems to cost it its distinctive status as a philosophical semantics. Second, the causal modeling semantics, but not the possible-worlds semantics, has the resources enough for accounting for both forward-tracking and backtracking counterfactual conditionals. (shrink)

Conducted 2 experiments with undergraduates which demonstrated that, in a recognition memory task, Ss recognized the negated antecedent and consequent propositions of previously encountered counterfactual conditionals significantly more often than control items, the latter effect being distinctly stronger (Exp I, n = 110). A similar result was obtained for causals related to previously encountered counterfactual conditionals and counterfactual conditionals related to previously encountered causals, the latter being the stronger effect (Exp II, n = 92). Results are discussed in the context (...) of the observation that a counterfactual conditional (a) presupposes the negation of its antecedent proposition, (b) strongly suggests the negation of its consequent proposition, and (c) is intimately related to a causal in which (a) and (b) are conjoined, with (a) taken as the grounds for (b). (PsycINFO Database Record (c) 2000 APA, all rights reserved) (unassigned). (shrink)

The present thesis focuses on counterfactuals. Specifically, we will address new questions and open problems that arise for the standard semantic accounts of counterfactual conditionals. The first four chapters deal with the Lewisian semantic account of counterfactuals. On a technical level, we contribute by providing an equivalent algebraic semantics for Lewis' variably strict conditional logics, which is notably absent in the literature. We introduce a new kind of algebra and differentiate between local and global versions of each of (...) Lewis' variably strict conditional logics. We study the algebraic properties of Lewis' logics and the structure theory of our newly introduced algebras. Additionally, we employ a new algebraic construction, based on the framework of Boolean algebras of conditionals, to provide an alternative semantics for Lewisian counterfactual conditionals. This semantic account allows us to establish new truth conditions for Lewisian counterfactuals, implying that Lewisian counterfactuals are definable conditionals, and each counterfactual can be characterized as a modality of a corresponding probabilistic conditional. We further extend these results by demonstrating that each Lewisian counterfactual can also be characterized as a modality of the corresponding Stalnaker conditional. The resulting formal semantic framework is much more expressive than the standard one and, in addition to providing new truth conditions for counterfactuals, it also allows us to define a new class of conditional logics falling into the broader framework of weak logics. On the philosophical side, we argue that our results shed new light on the understanding of Lewisian counterfactuals and prompt a conceptual shift in this field: Lewisian counterfactual dependence can be understood as a modality of probabilistic conditional dependence or Stalnakerian conditional dependence. In other words, whether a counterfactual connection occurs between A and B depends on whether it is "necessary" for a Stalnakerian/probabilistic dependence to occur between A and B. We also propose some ways to interpret the kind of necessity involved in this interpretation. The remaining two chapters deal with the probability of counterfactuals. We provide an answer to the question of how we can characterize the probability that a Lewisian counterfactual is true, which is an open problem in the literature. We show that the probability of a Lewisian counterfactual can be characterized in terms of belief functions from Dempster-Shafer theory of evidence, which are a super-additive generalization of standard probability. We define an updating procedure for belief functions based on the imaging procedure and show that the probability of a counterfactual A > B amounts to the belief function of B imaged on A. This characterization strongly relies on the logical results we proved in the previous chapters. Moreover, we also solve an open problem concerning the procedure to assign a probability to complex counterfactuals in the framework of causal modelling semantics. A limitation of causal modelling semantics is that it cannot account for the probability of counterfactuals with disjunctive antecedents. Drawing on the same previous works, we define a new procedure to assign a probability to counterfactuals with disjunctive antecedents in the framework of causal modelling semantics. We also argue that our procedure is satisfactory in that it yields meaningful results and adheres to some conceptually intuitive constraints one may want to impose when computing the probability of counterfactuals. (shrink)

Counterfactual analysis of causation between particular events, combined with standard semantics for counterfactual conditionals, cannot express the idea that the cause is sufficient for the effect. Several authors have suggested that a more complex pattern of nested counterfactual conditionals is a better candidate for expressing the idea of causal connection. The most systematic account is developed by Kadri Vihvelin. She argues that a complex pattern of causal dependence, expressed by embedded conditionals, covers all the cases of causation and still yields (...) an account of causal asymmetry. But the new account relies heavily on the use of backtracking conditionals, and no criterion is given for their evaluation. I will try to show that Vihvelin’s proposal is not superior to the standard account because it overlooks the disadvantages of a liberal theory of causal relata. (shrink)

The counterfactual tradition to defining actual causation has come a long way since Lewis started it off. However there are still important open problems that need to be solved. One of them is the (in)transitivity of causation. Endorsing transitivity was a major source of trouble for the approach taken by Lewis, which is why currently most approaches reject it. But transitivity has never lost its appeal, and there is a large literature devoted to understanding why this is so. Starting from (...) a survey of this work, we will develop a formal analysis of transitivity and the problems it poses for causation. This analysis provides us with a sufficient condition for causation to be transitive, a sufficient condition for dependence to be necessary for causation, and several characterisations of the transitivity of dependence. Finally, we show how this analysis leads naturally to several conditions a definition of causation should satisfy, and use those to suggest a new definition of causation. (shrink)

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an (...) add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning. (shrink)