Causal Reasoning, Misc

In Nozick’s rendition of the decision situation given in Newcomb’s Paradox dominance and the principle of maximum expected utility recommend different strategies. While evidential decision theory seems to be split over which principle to apply and how to interpret the principles in the first place, causal decision theory seems to go for the solution recommended by dominance. As a reply to the CDT proposal by Wolfgang Spohn, who opts for “one-boxing” by employing reflexive decision graphs, I will draw on the (...) framework of causal knowledge patterns, i.e., Bayes net causal models, augmented by non-causal knowledge, to finally arrive at “one-boxing” – more intuitively and more closely to what actually is in Nozick’s story. This proposal allows the careful re-examination of all relevant concepts in the original story and might cast new light on the following questions: How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge? How can the notion of prediction be analyzed – philosophically and formally? And what’s the decision-maker’s conceptualization of the situation he will act upon? (shrink)

Research in the psychology of causal thinking has frequently revealed effects of normative considerations on causal attributions, where participants tend to assign causality more strongly to agents who violate a norm in bringing about an outcome. Across several experiments, we show that it is possible to reverse this norm effect when the outcome in question is good rather than bad: in these cases, participants assign causality more strongly to a norm-conforming agent than to an agent who violates a norm. We (...) argue that this supports an explanation of the norm effect according to which it is due to a tendency to interpret statements of the form “X caused Y” or “X is the cause of Y” as assigning responsibility to X for bringing Y about. (shrink)

There is, among some scientists and philosophers, the idea that any theory that would allow the time travel would introduce causal issues. These types of temporal paradoxes can be avoided by the Novikov self-consistency principle or by a variation in the interpretation of many worlds with interacting worlds. The world in which we live has, according to David Lewis, a Parmenidean ontology: "a manifold of events in four dimensions," and the occupants of the world are the 4-dimensional aggregates of the (...) stages - "temporal lines". The causal loops in backwards time travel involve events that appear to "come from nowhere," paradoxical "self-existent" objects or information, resulting in a bootstrap paradox. Many believe that causality loops are not impossible or unacceptable, but only inexplicable. DOI: 10.13140/RG.2.2.28792.70407. (shrink)

There is a large literature exploring the effect of norms on the attribution of causation. Empirical research on this so-called “norm effect” has predominantly focused on two data points: A situation in which an agent violates a salient norm, and one in which there is no violation of a salient norm. Since the phenomenon is understood in bivalent terms (norm infraction vs. no norm infraction), most explanations thereof have the same structure. In this paper, we report several studies (total N=479) (...) according to which perceived causation depends on the strength of the norm violated – whether strength is conceived in terms of the norm’s strictness, explicitness or associated punishment. Consequently, the norm effect, properly conceived, is not bivalent but graded in nature, the standard data points (norm violation vs. no norm violation) are but a special case of a broader phenomenon. This, we argue, puts pressure on many, if not most, of the current explanations of the norm effect on causation. (shrink)

Recent work by Marchisella exposed a gap in the literature on causal non-monotonic reasoning: what is needed is an approach whose primary motivation is the formal representation of the way in which humans typically reason with cause and effect. We extend the work of Marchisella, and propose a new type of causal non-monotonic reasoning, _Plausible Causal Reasoning_, which fills the gap in the literature. We propose some new principles which help characterise Plausible Causal Reasoning, and suggest a family of non-monotonic (...) logics which provides a suitable starting point for its formalisation. (shrink)

To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations with artificial data are unsuited for assessing QCA. We take issue with either position in this impasse. On the one hand, we argue that data-driven evaluations of the correctness of (...) a procedure of causal inference require artificial data. On the other hand, we prove all previous attempts in this direction to have been defective. For the first time in the literature on configurational comparative methods, we lay out a set of formal criteria for an adequate evaluation of QCA before implementing a battery of inverse-search trials to test how this method performs in different recovery contexts according to these criteria. While our results indicate that QCA is correct when generating the parsimonious solution type, they also demonstrate that the method is incorrect when generating the conservative and intermediate solution type. In consequence, researchers using QCA for causal inference, particularly in human-sensitive areas such as public health and medicine, should immediately discontinue employing the method’s conservative and intermediate search strategies. (shrink)

When evaluating theories of causation, intuitions should not play a decisive role, not even intuitions in flawlessly-designed thought experiments. Indeed, no coherent theory of causation can respect the typical person’s intuitions in redundancy (pre-emption) thought experiments, without disrespecting their intuitions in threat-and-saviour (switching / short-circuit) thought experiments. I provide a deductively sound argument for these claims. Amazingly, this argument assumes absolutely nothing about the nature of causation. I also provide a second argument, whose conclusion is even stronger: the typical person’s (...) causal intuitions are thoroughly unreliable. This argument proceeds by raising the neglected question: in what respects is information about intermediate and enabling variables relevant to reliable causal judgment? (shrink)

Causal Bayes nets (CBNs) provide one of the most powerful tools for modelling coarse-grained type-level causal structure. As in other fields (e.g., thermodynamics) the question arises how such coarse-grained characterisations are related to the characterisation of their underlying structure (in this case: token-level causal relations). Answering this question meets what is called a “coherence-requirement” in the reduction debate: How are different accounts of one and the same system (or kind of system) related to each other. We argue that CBNs as (...) tools for type-level causal inference are abstract enough to roughly fit any current token-level theory of causation as long as certain modelling assumptions are satisfied, but accounts of actual causation, i.e. accounts that attempt to infer token-causation based on CBNs, for the very same reason, face certain limitations. (shrink)

This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (ASD). This (...) problem has to do with the individual’s assessment of the reliability of messages that are passed between people as common knowledge emerges. The proposal is developed on the background of our own empirical studies and outlines different ways common knowledge might be comprised. We discuss what these issues may tell us about ASD, about the relation between social and non-social cognition, about social objects, and about the dynamics of social networks. (shrink)

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

This paper outlines an Husserlian, phenomenological account of the first stages of the acquisition of empirical knowledge in light of some aspects of Wilfrid Sellars’ critique of the myth of the given. The account offered accords with Sellars’ in the view that epistemic status is attributed to empirical episodes holistically and within a broader normative context, but disagrees that such holism and normativity are accomplished only within the linguistic and conceptual confines of the space of reasons, and rejects the limitation (...) of the relevant normativity to the cognitive domain. Attention to the phenomenological notion of motivations in our mapping of the structure and acquisition of empirical knowledge reveals a form of weak categoriality given in experience, one outside exclusive mediation by language and concepts but also not merely causal. Section 1 outlines some basic aspects of Sellars’ account of empirical knowledge in Empiricism and the Philosophy of Mind, including a regress objection that arises due to his claim that empirical knowledge presupposes knowledge of general facts about perception. Section 2 examines Sellars’ later revisiting of the objection, via his critique of Roderick Firth, in “The Lever of Archimedes,” focusing on his analysis of the “Myth of the Categorial Given” and his use of the notion of ur-concepts. Section 3 looks at Sellars’ psychological nominalism and his rejection of the explanatory primacy of experience in light of the strict dichotomy between the space of causes and the space of reasons, and shows how Firth’s account aligns with phenomenology in its advocating for an irreducible and non-inferential role for experience. It also raises an important objection to Sellars’ account concerning the categorial givenness of causality. Section 4 turns to Husserl, arguing that his conception of motivation in the Logical Investigations and Ideas II reveals a third explanatory or logical space “between” that of causes and that of reasons. Section 5 further develops this account with regard to the explanatory role of lived experience. Section 6 revisits the regress objection to Sellars from the phenomenological standpoint developed earlier in the paper, and argues that an Husserlian account of empirical knowledge offers a viable alternative to Sellars’ that overcomes the regress objection and gives proper explanatory weight to the evidence of lived experience vis-à-vis scientific presuppositions about causality. (shrink)

Experiments have shown that prescriptive norms often influence causal inferences. The reason for this effect is still not clear. One problem of the studies is that the term ‘cause’ in the test questions is ambiguous and can refer to both the causal mechanism and the agent’s accountability. Possibly subjects interpreted the causal test question as a request to assess accountability rather than causality. Scenarios that put more stress on the causal mechanism should therefore yield no norm effect. Consequently, Experiment 1 (...) demonstrates that norms no longer influence causal judgments when the causal information is presented in a trial-by-trial learning task. Furthermore, Experiment 2 shows that norm effects are only obtained when the test question asks about a (potentially accountable) person but not when asked about a component of the causal mechanism. Both findings demonstrate that norms cease to influence causal judgments when the task settings highlight causal relations. (shrink)

Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. However, (...) I argue that this is only part of the epistemology of causal selection. Recent work on portable causes has implicitly assumed them to be portable within the same causal system at a later time. As a result, it has appeared that the objective content of causal selection includes only facts about the causal structure of that single system. In contrast, I present a case study from systems biology in which scientists are searching for causal factors that are portable across rather than within causal systems. By paying careful attention to how these biologists find portable causes, I show that the objective content of causal selection can extend beyond the immediate systems of interest. In particular, knowledge of the evolutionary history of gene networks is necessary for correctly identifying causal patterns in these networks that explain cellular behavior in a portable way. (shrink)

Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, problems arise for using safety as a condition on knowledge: safety is not necessary for knowledge and cannot always explain the Gettier cases and cases of statistical evidence it is meant to address. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: (...) causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one’s belief in p is causally safe if it is true in all causally relevant worlds where one believes p. Causal safety, I argue, can better explain the cases safety is meant to address and can avoid the arguments raised against the necessity of safety. (shrink)

Mary Shepherd (1777–1847) was a fierce and brilliant critic of Berkeley and Hume, who moreover offered strikingly original positive views about the nature of reality and our access to it which deserve much more attention (and credit, since she anticipates many prominent views) than they have received thus far. By way of illustration, I focus on Shepherd's 1824 Essay Upon the Relation of Cause and Effect, Controverting the Doctrine of Mr. Hume, Concerning the Nature of that Relation (ERCE). After a (...) brief setup, I canvas certain of her trenchant objections to Hume’s argumentation; I then present the positive core of her response to Hume, which consists in providing novel accounts of how reason alone or reason coupled with experience can justify, first, that every effect must have a cause, and second, that it is necessary that like causes produce like effects. Among other contributions here, Shepherd provides a distinctively metaphysical argument for the claim that nothing can begin to exist 'of itself' (going beyond an appeal to the Principle of Sufficient Reason, in particular), and leverages difference-making considerations to make the case that a single 'experimentum crucis' can justify causal belief (anticipating Mill's 'method of difference'). I close by highlighting salient features of Shepherd's metaphysics of causation, whereby causation is singularist and local (anticipating Ducasse and Anscombe) and involves synchronic interactions (anticipating Mill's and certain contemporary accounts), and according to which objects are essentially characterized by their causes and effects (anticipating contemporary causal or dispositional essentialist positions). (shrink)

I highlight a metaphysical concern that stands in the way of more widespread adoption of causal modeling techniques such as causal Bayes nets. Researchers in some fields may resist adoption due to concerns that they don't 'really' understand what they are saying about a system when they apply such techniques. Students in these fields are repeated exhorted to be cautious about application of statistical techniques to their data without a clear understanding of the conditions required for those techniques to yield (...) genuine insight into the data. They are acutely aware that anyone can chuck some data into a software package and get what looks like an answer, even though these tests may not be well-defined for the data on which they can apparently be run. This is thus a healthy skepticism for uptake of causal modeling methods, which points directly to the need for a metaphysical understanding of causation in order to successfully use the modeling methods. Without a clear understanding of what the methods are committing to, including what it means to say that there exists a causal relationship, researchers have limited ability to identify potentially bad output, or to independently verify results. (shrink)

We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly (...) increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought. (shrink)

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 (...) allows that the actual world is not the only world that is closest to the actual world. As a result, some counterfactuals that would otherwise have been true, turn out to be false. When these counterfactuals concern causation, fewer causal claims are true. In addition to describing steps towards solving the problem of profligate omissions, the proposal captures an abstraction that is shared by many of the existing solutions: depending on how the distance ordering underlying the Weak Centering condition is constructed and interpreted, some of these existing solutions can be recovered. (shrink)

The goal of perception is to infer the most plausible source of sensory stimulation. Unisensory perception of temporal order, however, appears to require no inference, since the order of events can be uniquely determined from the order in which sensory signals arrive. Here we demonstrate a novel perceptual illusion that casts doubt on this intuition: in three studies (N=607) the experienced event timings are determined by causality in real-time. Adult observers viewed a simple three-item sequence ACB, which is typically remembered (...) as ABC (Bechlivanidis & Lagnado, 2016), in line with principles of causality. When asked to indicate the time at which events B and C occurred, points of subjective simultaneity shifted so that the assumed cause B appeared earlier and the assumed effect C later, despite full attention and repeated viewings. This first demonstration of causality reversing perceived temporal order cannot be explained by post-perceptual distortion, lapsed attention, or saccades. (shrink)

A good definition of process tracing should highlight what is distinctive about process tracing as a methodology of causal inference. I look at eight criteria that are used to define process tracing in the methodological literature, and I dismiss all eight criteria as unhelpful (some because they are too restrictive, and others because they are vacuous). In place of these criteria, I propose four alternative criteria, and I draw a distinction between process tracing for the ultimate aim of testing a (...) start--end hypothesis versus process tracing as an ultimate end in itself. Although it is clear enough how the former method works, there is still much methodological work to be done in understanding the latter method as a distinctive method of causal inference, I argue. (shrink)

Research has probed the consequences of providing people with different types of information regarding why a person possesses a certain characteristic. However, this work has largely examined the consequences of different information subsets (e.g., information focusing on internal versus societal causes). Less work has compared several types of information within the same paradigm. Using the legal system as an example domain, we provided children (N=198 6- to 8-year-olds) with several types of information—including information highlighting internal moral character, internal biological factors, (...) behavioral factors, and societal factors—about why a specific outcome (incarceration) might occur. We examined how such language shaped children’s attitudes. In Study 1, children reported the most positivity toward people who were incarcerated for societal reasons and the least positivity toward people who were incarcerated for their internal moral character; attitudes linked with behavioral information fell between these extremes. Studies 2a-2b suggested that Study 1’s effects could not be fully explained by participants drawing different about individuals in Study 1. Study 3 replicated Study 1’s results and showed that information linking incarceration with internal biological factors led to more positivity than information linking incarceration with internal moral character. Finally, Study 4 suggested that the patterns found in Studies 1 and 3 generalize to non-punitive contexts. Moreover, Study 4 found that the effects in Studies 1 and 3 emerged regardless of whether information was communicated via explanations or descriptions. These results demonstrate that how we express our beliefs about social phenomena shape the realities in which others live. (shrink)

While practitioners think highly of randomized studies, some philosophers argue that there is no epistemic reason to randomize. Here I show that their arguments do not entail their conclusion. Moreover, I provide novel reasons for randomizing in the context of interventional studies. The overall discussion provides a unified framework for assessing baseline balance, one that holds for interventional and observational studies alike. The upshot: practitioners’ strong preference for randomized studies can be defended in some cases, while still offering a nuanced (...) approach to evidence-appraisal, one where not all non-randomized studies are treated equally. (shrink)

This paper aims to bridge philosophical and psychological research on causation, counterfactual thought, and the problem of backtracking. Counterfactual approaches to causation such as that by Lewis have ruled out backtracking, while on prominent models of causal inference interventionist counterfactuals do not backtrack. However, on various formal models, certain backtracking counterfactuals end up being true, and psychological evidence shows that people do sometimes backtrack when answering counterfactual questions in causal contexts. On the basis of psychological research, I argue that while (...) ordinarily both kinds of counterfactuals may be employed, non-backtracking counterfactuals are more easily used in causal inference because they are consistent with temporal order information embedded in the mental simulation heuristic, and they match reasoners’ experience of causation. While this approach is incompatible with the ambitions of counterfactual theories that seek to establish the non-backtracking interpretation as the only legitimate one, it can provide support for perspectival views on causation and open further inquiry on the functions of causal and counterfactual thought in the context of causal models. (shrink)

The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...) an interventionist perspective is to take advantage of the assumptions of common trend and continuity. (shrink)

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

"The first book that provides a systematic and rigorous logical theory of causality"--.

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are in tension with a (...) prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (shrink)

We blame faulty brakes for a car crash, or rain for our bad mood. This “merely causal” blame is usually seen as uninteresting. I argue that it is crucial for understanding the interpersonal blame with which we target ourselves and each other. The two are often difficult to distinguish, in a way that plagues philosophical discussions of blame. And interpersonal blame is distinctive, I argue, partly in its causal focus: its attention to a person as cause. I argue that this (...) causal focus helps explain several central characteristics of interpersonal blame: its tendency to exaggerate a person’s causal role, its weakening through attention to personal history or thoughts about determinism, its characteristic ‘force’ or ‘sting’, and our sense that blame is often harmful or unfair. I conclude by drawing out one implication of blame's causal focus: in a certain range of cases, blame is partly under our voluntary control. (shrink)

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

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

This paper argues that the nonidentity problem is really an artifact of faulty causal reasoning. In order to solve the nonidentity problem, we must determine that an agent causes a loss of happiness to another agent by means of an action that also causes the victim to exist. Woodward’s test for actual causation yields just this result. Equally crucial to solving the problem is the recognition that harms must be intentional and that intentionality is a function of norm-violation; this latter (...) position is defended by Holton. That the solution proposed is adequate is defended on the grounds that it yields intuitively correct judgments for a host of imaginary scenarios contemplated by theorists focused on determining what should count as harms. (shrink)

In cases in which many causes together bring about an effect, it is common to select some as particularly important. Philosophers since Mill have been pessimistic about analyzing this reasoning because of its variability and the multifarious causal and pragmatic details of how it works. I argue Mill was right to think these details matter but wrong that they preclude philosophical analysis of causal selection. I show that analyzing the pragmatic details of scientific debates about the important causes of the (...) Bhopal Gas Tragedy can illuminate causal reasoning about disasters and shed new light on causality and causal selection. (shrink)

People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm- violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects (...) are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment. (shrink)

When the absence of an event causes some outcome, it is an instance of omissive causation. For instance, not eating lunch may cause you to be hungry. Recent psychological proposals concur that the mind represents causal relations, including omissive causal relations, through mental simulation, but they disagree on the form of that simulation. One theory states that people represent omissive causes as force vectors; another states that omissions are representations of contrasting counterfactual simulations; a third argues that people think about (...) omissions by representing sets of iconic possibilities – mental models – in a piecemeal fashion. In this paper, we tease apart the empirical predictions of the three theories and describe experiments that run counter to two of them. Experiments 1 and 2 show that reasoners can infer temporal relations from omissive causes – a pattern that contravenes the force theory. Experiment 3 asked participants to list the possibilities consistent with an omissive cause – it found that they tended to list particular privileged possibilities first, most often, and faster than alternative possibilities. The pattern is consistent with the model theory, but inconsistent with the contrast hypothesis. We marshal the evidence and explain why it helps to solve a long-standing debate about how the mind represents omissions. (shrink)

The World Health Organization has issued international instructions for certification and classification (coding) of the coronavirus disease (COVID-19) as cause of death. Central to these instructions is the selection of the underlying cause of death for a public health preventive purpose. This article focuses on two rules for this selection: (1) that a death due to COVID-19 should be counted independently of pre-existing conditions that are suspected of triggering a severe course of COVID-19 and (2) that COVID-19 should not be (...) considered as due to anything else. The article argues that observance of the first rule may not always lead to an optimal selection from a preventive point of view and that in the future the ascertainment of an animal source of the coronavirus (SARS-CoV-2) would make it possible to reconceptualize ‘COVID-19‘ and create a zoonotic classification code by means of which a factor of a greater preventive value could be selected than what is currently possible. (shrink)

In the literature, the pragmatic dimension of metaphors has been clearly acknowledged. Metaphors are regarded as having different possible uses, and in particular, they are commonly viewed as instruments for pursuing persuasion. However, an analysis of the specific conversational purposes that they can be aimed at achieving in a dialogue and their adequacy thereto is still missing. In this paper, we will address this issue focusing on the distinction between the explanatory and persuasive goal. The difference between explanation and persuasion (...) is often blurred and controversial from a theoretical point of view. Building on the analysis of explanation in different theories and fields of study, we show how it can be conceived as characterized by a cognitive and a pragmatic dimension, where the transference of understanding is used pragmatically for different dialogical goals. This theoretical proposal will be applied to examples drawn from the medical context, to show how a pragmatic approach to explanation can account for the complexity of the cases that can be found in actual dialogical contexts. -/- . (shrink)

Recent philosophical work on causation has focused on distinctions across types of causal relationships. This paper argues for another distinction that has yet to receive attention in this work. This distinction has to do with whether causal relationships have “material continuity,” which refers to the reliable movement of material from cause to effect. This paper provides an analysis of material continuity and argues that causal relationships with this feature are associated with a unique explanatory perspective, are studied with distinct causal (...) investigative methods, and provide different types of causal control over their effects. (shrink)

Evidential Pluralism maintains that in order to establish a causal claim one normally needs to establish the existence of an appropriate conditional correlation and the existence of an appropriate mechanism complex, so when assessing a causal claim one ought to consider both association studies and mechanistic studies. Hitherto, Evidential Pluralism has been applied to medicine, leading to the EBM+ programme, which recommends that evidence-based medicine should systematically evaluate mechanistic studies alongside clinical studies. This paper argues that Evidential Pluralism can also (...) be fruitfully applied to the social sciences. In particular, Evidential Pluralism provides (i) a new approach to evidence-based policy; (ii) an account of the evidential relationships in more theoretical research; and (iii) new philosophical motivation for mixed methods research. The application of Evidential Pluralism to the social sciences is also defended against two objections. (shrink)

Memories often come with a feeling of pastness. The events we remember strike us as having occurred in our past. What accounts for this feeling of pastness? In his recent book, Memory: A self-referential account, Jordi Fernández argues that the feeling of pastness cannot be grounded in an explicit representation of the pastness of the remembered event. Instead, he argues that the feeling of pastness is grounded in the self-referential causal content of memory. In this paper, I argue that this (...) account falls short. The representation of causal origin does not by itself ground a feeling of pastness. Instead, I argue that we can salvage the temporal localization account of the feeling of pastness by describing a form of egocentric temporal representation that avoids Fernández’s criticisms. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...) is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...) on superstition that it is sometimes portrayed to be. Then, I offer an extended analysis of the second of the main arguments against divination given by Marcus. Inspired by Marcus’ second main argument, I formulate, explicate, and defend a substantive principle of scientific methodology that I call the “Ciceronian Causal-Nomological Requirement” (CCR). Roughly, this principle states that causal knowledge is essential for relying on correlations in predictive inference. Although I go on to argue that Marcus’ application of the CCR in his debate with Quintus is dialectically inadequate, I conclude that De Divinatione deserves its place in Cicero’s philosophical corpus, and that ultimately, its significance for the history and philosophy of science ought to be recognized. (shrink)

"A blab droid is a robot with a body shaped like a pizza box, a pair of treads, and a smiley face. Guided by an onboard video camera, it roams hotel lobbies and conference centers, asking questions in the voice of a seven-year-old. "Can you help me?" "What is the worst thing you've ever done?" "Who in the world do you love most?" People pour their hearts out in response. This droid prompts the question of what we can hope from (...) social robots. Might they provide humanlike friendship? Philosopher John Campbell doesn't think so. He argues that, while a social robot can remember the details of a person's history better than some spouses can, it cannot empathize with the human mind, because it lacks the faculty for thinking in terms of singular causation. Causation in Psychology makes the case that singular causation is essential and unique to the human species. From the point of view of practical action, knowledge of what generally causes what is often all one needs. But humans are capable of more. We have a capacity to imagine singular causation. Unlike robots and nonhuman animals, we don't have to rely on axioms about pain to know how ongoing suffering is affecting someone's ability to make decisions, for example, and this knowledge is not a derivative of general rules. The capacity to imagine singular causation, Campbell contends, is a core element of human freedom and of the ability to empathize with human thoughts and feelings"--. (shrink)

There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform all others across all possible scenarios. Coupling this with the fact that an analyst can never know which approach is (...) best for their particular data, we propose a synthetic estimator that averages over a set of candidate estimators. Averaging is widely used in statistics for problems such as prediction, where there are many possible models, and averaging can improve performance and increase robustness to using incorrect models. We show that these ideas carry over into the estimation of causal effects in high-dimensional scenarios. We show theoretically that averaging provides robustness against choosing a bad model, and show empirically via simulation that the averaging estimator performs quite well, and in most cases nearly as well as the best among all possible candidate estimators. Finally, we illustrate these ideas in an environmental wide association study and see that averaging provides the largest benefit in the more difficult scenarios that have large numbers of confounders. (shrink)

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 (...) good skeptical grounds for resisting any conclusions in favour of the automation of causal reasoning. If by causal reasoning is meant the entirety of the process through which we discover causal relationships and make use of this knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals, then one relies besides on tacit knowledge, as might be constituted by or derived from the epistemic faculty virtues and abilities of the causal reasoner, the value systems and character traits of the causal reasoner, the implicit knowledge base available to the causal reasoner, and the habits that sustain our causal reasoning practices. While certain aspects of causal reasoning may be axiomatized and formalized and algorithms may be implemented to approximate causal reasoning, one has to remain skeptical about whether causal reasoning may be fully automated. This demonstration will involve an engagement with Meno’s Paradox. (shrink)

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity (...) of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, (...) we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process‐level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it. (shrink)

During the first half of the twentieth century, many philosophers of memory opposed the postulation of memory traces based on the claim that a satisfactory account of remembering need not include references to causal processes involved in recollection. However, in 1966, an influential paper by Martin and Deutscher showed that causal claims are indeed necessary for a proper account of remembering. This, however, did not settle the issue, as in 1977 Malcolm argued that even if one were to buy Martin (...) and Deutscher’s argument for causal claims, we still don’t need to postulate the existence of memory traces. This paper reconstructs the dialectic between realists and anti-realists about memory traces, suggesting that ultimately realists’ arguments amount to inferences to the best explanation. I then argue that Malcolm’s anti-realist strategy consists in the suggestion that causal explanations that do not invoke memory traces are at least as good as those that do. But then, Malcolm, I argue that there are a large number of memory phenomena for which explanations that do not postulate the existence of memory traces are definitively worse than explanations that do postulate them. Next, I offer a causal model based on an interventionist framework to illustrate when memory traces can help to explain memory phenomena and proceed to substantiate the model with details coming from extant findings in the neuroscience of memory. (shrink)