In the last third of the twentieth century, humanists and social scientists argued that attention to genetics would heighten already‐existing genetic determinism, which in turn would intensify negative social outcomes, especially sexism, racism, ableism, and harshness to criminals. They assumed that laypeople are at risk of becoming genetic essentialists. I will call this the “laypeople are genetic essentialists model.” This model has not accurately predicted psychosocial impacts of findings from genetics research. I will be arguing that the failure of the (...) model can be traced to its inability to recognize the complexity of laypeople’s attitudes; its incorrect theory of how beliefs, attitudes, and discourse function; and its blindness to how academics’ own interests can override the available evidence. More specifically, I suggest that the substantial data about laypeople’s deployment of genetics supports what I will call the “laypeople are strategic essentialists model” better than the “laypeople are genetic essentialists model.” The strategic essentialists model holds that people tend to store multiple categories, including multiple causal forces, that they deploy “strategically” to serve context‐dependent goals. It will be difficult for academics to reorient ourselves to model laypeople as sophisticated strategic essentialists rather than as naïve genetic essentialists. Perhaps a little shift, however, will be of value. (shrink)

Anorexia nervosa remains an enigma among Western cultures. Various causal explanations have been offered, encompassing biological, psychological, and sociocultural models. These explanations, however, focus on the immediate or proximal mechanisms of causation. A more thorough understanding of anorexia nervosa can be achieved by understanding the relationship between these factors and ultimate causation, the level of explanation which deals with individual reproductive fitness. This paper reviews the biological, psychological, sociocultural, and evolutionary models and indicates a necessary synthesis between proximate and (...) ultimate levels of causation in examining the anorexia nervosa puzzle. (shrink)

Among theories of personal identity over time the simple view has not been popular among philosophers, but it nevertheless remains the default view among non philosophers. It may be construed either as the view that nothing grounds a claim of personal identity over time, or that something quite simple (a soul perhaps) is the ground. If the former construal is accepted, a conspicuous difficulty is that the condition of causal dependence between person-stages is absent. But this leaves such a (...) view open to an objection from the failure to provide a condition of individuation. If, on the other hand something like a soul is said to ground personal identity over time, such an account turns out to be more suited to a kind of continuity view. (shrink)

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

Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial (...) of the Antecedent, and Affirmation of the Consequent—causal conditional reasoning data are only partially able to support these principles. We present three experiments that use concrete and abstract causal scenarios and combine inference tasks with a new type of task in which people reformulate a given causal situation. The results provide evidence for the proposed representational principles. Implications for theories of the na ve understanding of causality are discussed. (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.

In the present work it will be shown that a Theory of Everything presupposes a quantization of space and time in order to uncover the law of causality as a hidden parameter of human cognition, that nature destroying comes into effect. This involves not only a re-evaluation of the reduced Planck constant, but also and above all a new interpretation of Heisenberg’s uncertainty principle as the basic description of natural behaviour. The proof is the decoding of 137.

Disjunctivists maintain that perceptual experiences and hallucinatory experiences are distinct kinds of event with different metaphysical natures. Moreover, given their view about the nature of perceptual cases, disjunctivists must deny that the perceptual kind of experience can occur during hallucination. However, it is widely held that disjunctivists must grant the converse claim, to the effect that the hallucinatory kind of experience occurs even during perception. This paper challenges that thought. As we will see, the argument for thinking that the hallucinatory (...) kind of experience is present even in cases of perception depends on prior acceptance of a ‘non-demanding’ conception of hallucination, on which all it takes to produce an hallucinatory experience is to induce in the subject the right kind of neurological condition. On the view developed here, by contrast, there are substantive causal conditions, going beyond the mere occurrence of the right kind of neurological state, that must be met if an experience of the hallucinatory kind is to occur. By drawing on this view, I argue, disjunctivists can deny that the kind of experience involved in hallucination ever occurs during perception. This then allows disjunctivists to avoid certain important problems they would otherwise face. It also leaves them with considerably more freedom when it comes to theorising about the nature of hallucinatory experience. (shrink)

Sequel to Armendt 1986, ‘A Foundation for Causal Decision Theory.’ The representation theorem for causal decision theory is slightly revised, with the addition of a new restriction on lotteries and a new axiom (A7). The discussion gives some emphasis to the way in which appropriate K-partitions are characterized by relations found among the agent’s conditional preferences. The intended interpretation of conditional preference is one that embodies a sensitivity to the agent’s causal beliefs.

We put forth an account for when to believe causal and evidential conditionals. The basic idea is to embed a causal model in an agent’s belief state. For the evaluation of conditionals seems to be relative to beliefs about both particular facts and causal relations. Unlike other attempts using causal models, we show that ours can account rather well not only for various causal but also evidential conditionals.

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)

Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model of (...) conditional learning. This model extends traditional Bayesian learning by incorporating causal models into agents’ epistemic states. On this theory, conditional learning proceeds in two steps. First, an agent learns a new causal model with the appropriate relationship between the antecedent and the consequent. Then, the agent narrows down the set of possible worlds to include only those which make the conditional proposition true. This model of learning can incorporate both standard cases of Bayesian learning and the non-uniform learning required to learn conditional information. (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)

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)

The appropriateness, or acceptability, of a conditional does not just ‘go with’ the corresponding conditional probability. A condition of dependence is required as well. In this paper a particular notion of dependence is proposed. It is shown that under both a forward causal and a backward evidential reading of the conditional, this appropriateness condition reduces to conditional probability under some natural circumstances. Because this is in particular the case for the so-called diagnostic reading of the conditional, this analysis might (...) help to explain some of Douven and Verbrugge’s empirical observations. (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)

This study examined the hypothesis that a key process in conditional reasoning with concrete premises involves on-line retrieval of information about potential alternate antecedents. Participants were asked to solve reasoning problems with causal conditional premises (If cause P then effect Q). These premises were inserted into short contexts. The availability of potential alternatives was varied from one context to another by adding statements that explicitly invalidated one or more of these alternatives (i.e., other causes that lead to the effect (...) Q). The invalidated alternatives differed in the degree of their semantic association to the consequent term (Q). The results show that the effect of invalidating one or more potential alternatives on the two uncertain logical forms, AC and DA, was largely determined by their relative associative strength. These results strongly support a model for conditional reasoning with causal premises that supposes that a key element in responding to the uncertain logical forms is on-line retrieval of at least one potential alternative antecedent. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility (...) of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit (...) of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms. (shrink)

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.

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

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.

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)

This article defends the notion of downward causation, relating it to a notion of conditional independence.

Advocates of Causal Decision Theory (CDT) argue that Evidential Decision Theory (EDT) is inadequate because it gives the wrong result in Newcomb problems. Egan (2007) provides a recipe for converting Newcomb problems to counterexamples to CDT, arguing that CDT is inadequate too. Proposed by Edgington (2011), the Conditional Causal Decision Theory (CCDT) is widely taken uncritically in the recent literature as a version of CDT that conforms to the supposedly correct pre-theoretic judgments about the rationality of acts in (...) Newcomb problems and Egan cases. While the renewed attention to the debate between EDT and CDT is mostly focused on how various theories treat Egan cases, this paper scrutinizes Edgington’s treatment of Newcomb problems and argues that, despite Edgington’s promise, CCDT actually fails in Newcomb problems. This, I assume, shows that CCDT is superior neither to the classic visions of CDT nor to EDT. The paper concludes by arguing, in general, that CCDT is systematically bound to deliver the same results as does EDT in any decision problem. Despite being formulated in terms of causal notions, therefore, it collapses back into the classic EDT. (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)

This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.

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)

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.