A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a 'structural strategy', the goal (...) of which is to model and test causal structures that explain correlational data; second, a 'manipulationist or interventionist strategy', that hinges upon the notion of invariance under intervention. It is argued that while the former can offer a solution the latter cannot. (shrink)

This is a response to the response by Robyn Bluhm to my paper, and I am again arguing for a limited role of capacities in psychiatry, given the current scientific uncertainties.

Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive (...) causal explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

Carl Hempel argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon and Richard Jeffrey argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and (...) defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

This chapter focuses on the experimental practices and reasoning strategies employed in nineteenth century investigations on the causal origin of the phenomenon of Brownian movement. It argues that there was an extensive and sophisticated experimental work done on the phenomenon throughout the nineteenth century. Investigators followed as rigorously as possible the methodological standards of their time to make causal claims and advance causal explanations of Brownian movement. Two major methodological strategies were employed. The first was the experimental (...) strategy of varying the circumstances. Suspected causal factors were varied and the resulting effect on Brownian movement was studied. The main goal of this strategy was the identification of difference-making factors, i.e., factors having a causal influence on the phenomenon. The second was the so-called method of hypothesis. Rather than relying exclusively on the ability of experiments to identify difference-making factors, its proponents tried to show how the independently developed tenets of the new kinetic-molecular conception of matter provided a plausible causal explanation of Brownian movement. Each one of these methodological strategies had its distinctive practices and notions of control. None of these strategies could, on its own, establish molecular motion as the cause of Brownian movement. It was only the fruitful combination of the two strategies and of their accompanying practices and notions of control that led, at the end of the nineteenth century, to the recognition of molecular motion as the most probable cause of Brownian movement. (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)

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine (...) their underlying structures. Each puzzle comprised a three‐ or four‐node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information‐seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas. (shrink)

In this paper I look at causality in the context of intervention research, and discuss some problems faced in the evaluation of causal hypotheses via interventions. I draw attention to a simple problem for evaluations that employ randomized controlled trials. The common alternative to randomized trials, the observational study, is shown to face problems of a similar nature. I then argue that these problems become especially acute in cases where the intervention is complex (i.e. that involves intervening in (...) a complex system). Finally, I consider and reject a possible resolution of the problem involving the simulation of complex interventions. The conclusion I draw from this is that we need to radically reframe the way we think about causal inference in complex intervention research. (shrink)

The causal nature of evolution is one of the central topics in the philosophy of biology. The issue concerns whether equations used in evolutionary genetics point to some causal processes or purely phenomenological patterns. To address this question the present article builds well-defined causal models that underlie standard equations in evolutionary genetics. These models are based on minimal and biologically plausible hypotheses about selection and reproduction, and generate statistics to predict evolutionary changes. The causal reconstruction (...) of the evolutionary principles shows adaptive evolution as a genuine causal process, where fitness and selection are both causes of evolution. 1 Introduction2 The Philosophical Puzzle3 Causal Models4 Causal Foundations of Evolutionary Genetics4.1 Univariate quantitative genetics model4.1.1 The causal graph4.1.2 Structural equations4.1.3 Deriving evolutionary transition functions4.2 One-locus population genetics model5 Evolution as a Causal Process5.1 Selection as a causal process5.2 Causes of evolutionary change6 Conclusion. (shrink)

There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first statement of his notorious theorem in 1966. As is well known, its focus has mainly been the so-called Einstein-Podolsky-Rosen (“EPR”) thought experiment, and the ensuing observed correlations in real EPR like experiments. But although implicitly the discussion goes as far back as Bell’s work, it is only in the last two (...) decades that it has become recognizably and explicitly a debate about causal inference in the quantum realm. The bulk of this paper is devoted to a review of three influential arguments in the philosophical literature that aim to show that causal models for the EPR correlations are impossible, due to Bas Van Fraassen, Daniel Hausman and Huw Price. I contend that all these arguments are inconclusive since they contain premises or presuppositions that are false, unwarranted, or at least controversial. Five different common cause models are outlined that seem perfectly viable for the EPR correlations. These models are then employed to illustrate various difficulties with the premises and presuppositions underlying Van Fraassen’s, Hausman’s and Price’s arguments. In all these cases it is argued that the difficulties cut deep against these authors’ own theories of causation and causal inference. My conclusions are that causal models for the EPR correlations remain viable, that philosophical work is still required to assess their relative virtues, and that in any case the mere theoretical conceivability and empirical possibility of these models sheds doubts over Van Fraassen’s, Hausman’s and (important elements in) Price’s theories of causation and causal inference. (shrink)

While scientific realism generally assumes that successful scientific explanations yield information about reality, realists also have to admit that not all information acquired in this way is equally well warranted. Some versions of scientific realism do this by saying that explanatory posits with which we have established some kind of causal contact are better warranted than those that merely appear in theoretical hypotheses. I first explicate this distinction by considering some general criteria that permit us to distinguish (...) class='Hi'>causal warrant from theoretical warrant. I then apply these criteria to a specific case from particle physics, claiming that scientific realism has to incorporate the distinction between causal and theoretical warrant if it is to be an adequate stance in the philosophy of particle physics. (shrink)

The causal nature of evolution is one of the central topics in the philosophy of biology. The issue concerns whether equations used in evolutionary genetics point to some causal processes or purely phenomenological patterns. To address this question the present article builds well-defined causal models that underlie standard equations in evolutionary genetics. These models are based on minimal and biologically plausible hypotheses about selection and reproduction, and generate statistics to predict evolutionary changes. The causal reconstruction (...) of the evolutionary principles shows adaptive evolution as a genuine causal process, where fitness and selection are both causes of evolution. 1 Introduction2 The Philosophical Puzzle3 Causal Models4 Causal Foundations of Evolutionary Genetics4.1 Univariate quantitative genetics model4.1.1 The causal graph4.1.2 Structural equations4.1.3 Deriving evolutionary transition functions4.2 One-locus population genetics model5 Evolution as a Causal Process5.1 Selection as a causal process5.2 Causes of evolutionary change6 Conclusion. (shrink)

Many philosophers believe that there is a causal condition on perception, and that this condition is a conceptual truth about perception. A highly influential argument for this claim is based on intuitive responses to Gricean style thought experiments. Do the folk share the intuitions of philosophers? Roberts et al. (2016) presented participants with two kinds of cases: Blocker cases (similar to Grice’s case involving a mirror and a pillar) and Non-Blocker cases (similar to Grice’s case involving a clock and (...) brain stimulation). They found that a substantial minority agreed that seeing occurs in the Non-Blocker cases, and that in the Blocker cases significantly less agreed that seeing occurs. They thus hypothesized that folk intuitions better align with a no blocker condition than with a causal condition. This paper continues this line of enquiry with two new experiments. The paper investigates the generality and robustness of Roberts et al.’s findings by expanding the sense modalities tested from only vision to audition and olfaction as well. The paper also uses Gricean style thought experiments as a case study for investigating the “reflection defense” against the negative project in experimental philosophy. Our results replicate and extend Roberts et al.’s study and support their hypothesis that folk intuitions better align with a no blocker condition. They also provide an empirical reason to doubt the reflection defense. (shrink)

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

The paper argues that on three out of eight possible hypotheses about the EPR experiment we can construct novel and realistic decision problems on which (a) Causal Decision Theory and Evidential Decision Theory conflict (b) Causal Decision Theory and the EPR statistics conflict. We infer that anyone who fully accepts any of these three hypotheses has strong reasons to reject Causal Decision Theory. Finally, we extend the original construction to show that anyone who gives any (...) of the three hypotheses any non-zero credence has strong reasons to reject Causal Decision Theory. However, we concede that no version of the Many Worlds Interpretation (Vaidman, in Zalta, E.N. (ed.), Stanford Encyclopaedia of Philosophy 2014) gives rise to the conflicts that we point out. (shrink)

We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three (...) experiments that test the prediction that bottom-up inference should occur only when the actor's primary objective is known. The model fits intentionality judgments reasonably well with no free parameters. (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)

The distinction between causal role and selected effect functions is typically framed in terms of their respective explanatory roles. However, much of the controversy over functions in genomics takes place in an investigative, not an explanatory context. Specifically, the process of component-driven functional investigation begins with the designation of some genetic or epigenetic element as functional —i.e. not junk— because it possesses properties that, arguably, suggest some biologically interesting organismal effect. The investigative process then proceeds, in a bottom-up fashion, (...) to search for those effects. I argue that this process encounters a problem reminiscent of one that Gould and Lewontin associated with the adaptationist program. Just as their stereotypical adaptationst became trapped in the myopic pursuit of one selectionist hypothesis after another, so can the investigation of CR functions in genomics lead to an unending series putative organism-level CR functions for junk DNA. This is an acute problem for genomics, because eukaryotic genomes are littered with transposable elements and their deactivated descendants which often masquerade as interesting CR-functional components and it is experimentally onerous to determine whether they lack such a function. I further argue that selectionist reasoning about TE-host coevolutionary dynamics can greatly streamline the investigative process. Importantly, selectionist hypotheses need not be well confirmed to be illuminating in this context. Informed selectionist reasoning about the strategic roles of TEs in the genome offers a corrective to the idea that most of our DNA is somehow CR functional for the organism. (shrink)

This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not suffice (...) for correct inferences of causal structure, I show it to serve as a preliminary understanding of causal concepts as asymmetric, that later incorporates other types of evidence. This approach supplies causal models with an asymmetric concept of causation that underlies hypotheses about causal structure, as I will illustrate from the framework of the knowledge-based causal induction model. I further argue for an integrating perspective, showing how the understanding of causes as preceding their effects underlies both psychological models and philosophical debates over time and the causal asymmetry, particularly regarding problem cases such as simultaneous causation or backwards causation, and the conceptual connection between causation and action. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to (...) class='Hi'>causal inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

What do the words "ceteris paribus" add to a causal hypothesis, that is, to a generalization that is intended to articulate the consequences of a causal mechanism? One answer, which looks almost too good to be true, is that a ceteris paribus hedge restricts the scope of the hypothesis to those cases where nothing undermines, interferes with, or undoes the effect of the mechanism in question, even if the hypothesis's own formulator is otherwise unable to specify fully what (...) might constitute such undermining or interference. I will propose a semantics for causal generalizations according to which ceteris paribus hedges deliver on this promise, because the truth conditions for a causal generalization depend in part on the—perhaps unknown—nature of the mechanism whose consequences it is intended to describe. It follows that the truth conditions for causal hypotheses are typically opaque to the very scientists who formulate and test them. (shrink)

Semmelweis’s discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have used the episode to illustrate, appraise and compare views of proper scientific methodology.Here I argue that the episode can be profitably reexamined in light of two cognate notions: causal reasoning and mechanisms. Semmelweis used several causal reasoning strategies both to support his own and to reject competing hypotheses. (...) However, these strategies have gone unappreciated in the existing literature. I show that a causal reasoning approach makes sense of the multitude of tables in Semmelweis’s main text, which in later editions were often abridged because they appeared redundant.Moreover, the existing literature tends to focus on Semmelweis’s clinical intervention and on the extent to which it alone confirms his theoretical conclusions. This neglects Semmelweis’s efforts to show by animal experiments that his clinical results are in agreement with a demonstrable mechanism of puerperal fever pathogenesis. I argue that the full evidential force of Semmelweis’s argument can only be appreciated if both his clinical and his laboratory investigations are taken into account. (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)

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation (...) implies conjunctive causation and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (shrink)

In this work we try to study theories of causation based upon causal processes and causal interactions in the context of classical and quantum physics. Our central aim is to find out whether such causal theories are compatible with the world picture suggested by contemporary theories of physics. In the first part, we review, compare and try to place among more general taxonomical schemes, the causal theories by Russell (the causal lines approach), Reichenbach (mark method, (...) probabilistic causality and the principle of common cause), Salmon (mark method, structure, invariant and conserved quantities approaches) and Dowe (conserved quantity theory). In the second part of the work, we deal with some problems that process theories of causation face whenever one tries to define, rigorously, causal concepts (e.g. “causal process” or “conjunctive common cause”) in the context of classical relativistic physical theories (van Dam – Wigner particle theory, the free Klein-Gordon field, classical electromagnetic theory) as well as quantum theories (algebraic relativistic quantum field theory). In this vein, special attention has been given to the locality problem in physical theories as it emerges from the structure of local classical the quantum theories (semantic locality), the relation between global and local conditions for physical systems (global and local determinism) as well as the problem of action at a distance, which is of central importance for the theories of causation under examination (Bell’s inequalities, EPR paradox, Reeh-Schlieder theorem and local independence conditions). We argue for three different theses: first, there are physical theories in the context of which it is impossible even to formulate the necessary physical hypotheses that allow us to define basic causal concepts and causal principles; second, there are physical theories which allow us to formulate these hypotheses only ad hoc; finally, in some cases it is necessary to revise basic causal principles and concepts bequeathed by the philosophical tradition. (shrink)

Joël Dolbeault | : D’abord, nous expliquons comment Bergson caractérise la liberté, et pourquoi celle-ci s’oppose à la fois au déterminisme et au hasard. Ensuite, nous montrons que la théorie bergsonienne de la liberté repose principalement sur l’idée que les états psychiques ne sont pas les occurrences de certains types, ce qui conduit à penser que leur apparition n’est pas gouvernée par l’action de lois. L’acte libre est causé par un sujet empirique, mais cette causalité n’est pas gouvernée par des (...) lois. Puis, nous soulignons l’originalité de cette théorie, notamment le fait qu’elle rejette le principe des possibilités alternatives, défendu par la plupart des libertariens actuels. À la fin de l’article, nous examinons plusieurs objections possibles contre cette théorie, en particulier l’objection inspirée par les expériences de Libet, ainsi que les réponses possibles. | : To begin with, we explain how Bergson characterizes free will and why it is opposed to both determinism and chance. Then we show that Bergson’s theory of free will is primarily based on the idea that mental states are not occurrences of certain types, which suggests that their appearance is not governed by the action of laws. The free act is caused by an empirical subject, but this causality is not governed by laws. Next, we highlight the originality of this theory, including the fact that it rejects the principle of alternative possibilities, defended by most of libertarians today. At the end of the article, we examine several possible objections against this theory, particularly the objection inspired by Libet’s experiments, and the possible answers. (shrink)

I argue that the Causal Markov Condition (CMC) is in principle applicable to the Einstein–Podolsky–Rosen (EPR) correlations. This is in line with my defence in the past of the applicability of the Principle of Common Cause to quantum mechanics. I first review a contrary claim by Dan Hausman and Jim Woodward, who endeavour to preserve the CMC against a possible counterexample by asserting that the conditions for the application of the CMC are not met in the EPR experiment. In (...) their view the CMC is inapplicable to the EPR correlations—i.e. it neither obtains nor fails. The view is grounded upon the non-separability of the quantum state, and the consequent unavailability of interventions. I urge that whether interventions are available in EPR—and why—is a complex and contextual question that does not have a unique or uniform answer. Instead, I argue that different combinations of causal hypotheses under test and interpretations of quantum mechanics yield different answers to the question. (shrink)

Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not of itself a reliable guide to judgements about policy, which inevitably involve causal conclusions. The policy implications of empirical data can be completely reversed by alternative hypotheses about the causal relations of variables, and the estimates of a particular causal influence can be radically altered by changes in the assumptions made about other dependencies.2 (...) For these reasons, one of the common aims of empirical research in the.. (shrink)

One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available, participants preferred to search for these cues first and to (...) base their decisions on them. When allowed to learn cue validities in addition to causal information, participants also became more frugal, made more accurate decisions, and were more precise in estimating cue validities than was a control group that did not receive causal information. These results can be attributed to the causal relation between the cues and the criterion, rather than to greater saliency of the causal cues. Overall, our results support the hypothesis that causal knowledge aids in the learning of cue validities and is treated as a meta-cue for identifying highly valid cues. (shrink)

We examined the association of parents’ genetic causal beliefs and parenting behaviors, hypothesizing a positive association between parents’ genetic causal beliefs and their use of psychological control. Study 1 (N = 394) was a cross-sectional survey and revealed that parents’ genetic essentialism beliefs were positively associated with their self-reported use of harsh psychological control, but only for parents who reported relatively high levels of problem behaviors in their children. Study 2 (N = 293) employed a 4-day longitudinal design (...) and revealed that parents’ genetic causal beliefs positively predicted the use of psychological control, especially on days when they perceived relatively high problem behaviors in children. Overall, the studies demonstrated that parents’ genetic causal beliefs about character positively predicted psychologically controlling and harsh responses to child problem behaviors, which may ultimately be detrimental to child development. (shrink)

Transcranial magnetic stimulation is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are (...) sufficient to warrant strong inferences about the direct causal properties of targeted brain structures. Overcoming these limitations demands a close look at the designs of rTMS studies, especially the methodological and theoretical conditions which are necessary for the functional decomposition of the relations between brain areas and cognitive functions. The main points of this article are that TMS-based inferences are limited in that stimulation-related causal effects are not equivalent to structure-related causal effects due to TMS side effects, the electric field distribution, and the sensitivity of neuroimaging and behavioral methods in detecting structure-related effects and disentangling them from confounds. Moreover, the postulated causal effects can be based on indirect effects. A few suggestions on how to manage some of these limitations are presented. We discuss the benefits of combining rTMS with neuroimaging in experimental reasoning and we address the restrictions and requirements of rTMS control conditions. The use of neuroimaging and control conditions allows stronger inferences to be gained, but the strength of the inferences that can be drawn depends on the individual experiment’s designs. Moreover, in some cases, TMS might not be an appropriate method of answering causality-related questions or the hypotheses have to account for the limitations of this technique. We hope this summary and formalization of the reasoning behind rTMS research can be of use not only for scientists and clinicians who intend to interpret rTMS results causally but also for philosophers interested in causal inferences based on brain stimulation research. (shrink)

The paper investigates the question as to which features of hypotheses make them explanatory. Given the intuitive appeal of causal explanations, one might suspect that explanatoriness is deeply connected with causation. I argue in detail that this is wrong by showing that none of the dominant analyses of causation are suited for general accounts of explanatoriness. In the second part, I provide the outlines of an account of explanatoriness that connects it with scientific understanding, which in turn is (...) argued to be analyzable as the cognitive realization of scientific models. (shrink)

This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of (...) science. At one end of the philosophical spectrum, my thesis undermines the views of those who, like Cartwright (Erkenntnis 70:45–58, 2009), follow Hesse (Models and Analogies in Science, Notre Dame, University of Notre Dame Press, 1963) in restricting the possibility of learning from models to only those situations where a model identifies some causal factors present in the target. At the other end of the spectrum, my thesis also helps undermine some extremely permissive positions, e.g., Grüne-Yanoff’s (Erkenntnis 70(1):81–99, 2009, Philos Sci 80(5): 850–861, 2013) claim that learning from a model is possible even in the absence of any similarity at all between model and target. The thesis that we can learn from non-causal models offers a cautious middle ground between these two extremes. (shrink)

An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists--who generally can't run controlled experiments to test and validate their hypotheses--apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases (...) in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages. - -. (shrink)

nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...) tens of thousands, and there is often limited background knowledge to reduce the space of alternative causal hypotheses. In such domains, non-automated causal discovery techniques appear to be hopeless, while the availability of faster computers with larger memories and disc space allow for the practical implementation of computationally intensive automated search algorithms over large search spaces. Contemporary science is not your grandfather’s science, or Karl Popper’s. Causal inference without experimental controls has long seemed as if it must somehow be capable of being cast as a kind of statistical inference involving estimators with some kind of convergence and accuracy properties under some kind of assumptions. Until recently, the statistical literature said not. While parameter estimation and experimental design for the effective use of data developed throughout the 20th century, as recently as 20 years ago the methodology of causal inference without experimental controls remained relatively primitive. Besides a cessation of hostilities from the majority of the statistical and philosophical communities (which has still only partially happened), several things were needed for theories of causal estimation to appear and to flower: well defined mathematical objects to represent causal relations; well defined connections between aspects of these objects and sample data; and a way to compute those connections. A sequence of studies beginning with Dempster’s work on the factorization of probability distributions [Dempster 1972] and culminating with Kiiveri and Speed’s [Kiiveri & Speed 1982] study of linear structural equation models, provided the first, in the form of directed acyclic graphs, and the second, in the form of the “local” Markov condition.. (shrink)

The formal definition of species as explanatory hypotheses presented by Fitzhugh is emended. A species is an explanatory account of the occurrences of the same character among gonochoristic or cross-fertilizing hermaphroditic individuals by way of character origin and subsequent fixation during tokogeny. In addition to species, biological systematics also employs hypotheses that are ontogenetic, tokogenetic, intraspecific, and phylogenetic, each of which provides explanatory hypotheses for distinctly different classes of causal questions. It is suggested that species (...) class='Hi'>hypotheses can not be applied to organisms with obligate asexual, parthenogenetic, and self-fertilizing modes of reproduction. Hypotheses explaining shared characters among such organisms are, instead, strictly phylogenetic. Several implications of this emended definition are examined, especially the relations between species, intraspecific, and phylogenetic hypotheses, as well as the limitations of species names to be applied to temporally different characters within populations. (shrink)

Many philosophers believe that there is a causal condition on perception, and that this condition is a conceptual truth about perception. A highly influential argument for this claim is based on intuitive responses to Gricean-style thought experiments. Do the folk share the intuitions of philosophers? Roberts et al. (2016) presented participants with two kinds of cases: Blocker cases (similar to Grice’s case involving a mirror and a pillar) and Non-Blocker cases (similar to Grice’s case involving a clock and brain (...) stimulation). They found that a substantial minority agreed that seeing occurs in the Non-Blocker cases, and that in the Blocker cases significantly less agreed that seeing occurs. They thus hypothesized that folk intuitions better align with a no blocker condition than with a causal condition. This paper continues this line of enquiry with two new experiments. The paper investigates the generality and robustness of Roberts et al.’s findings by expanding the sense modalities tested from only vision to audition and olfaction as well. The paper also uses Gricean-style thought experiments as a case study for investigating the “reflection defense” against the negative project in experimental philosophy. Our results replicate and extend Roberts et al.’s study and support their hypothesis that folk intuitions better align with a no blocker condition. They also provide an empirical reason to doubt the reflection defense. (shrink)

Sober (1984) has considered the problem of determining the evidential support, in terms of likelihood, for a hypothesis that is incomplete in the sense of not providing a unique probability function over the event space in its domain. Causal hypotheses are typically like this because they do not specify the probability of their initial conditions. Sober's (1984) solution to this problem does not work, as will be shown by examining his own biological examples of common cause explanation. The (...) proposed solution will lead to the conclusion, contra Sober, that common cause hypotheses explain statistical correlations and not matchings between event tokens. (shrink)

Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct. The Common Factor Model models the relations between the observed indicators and the latent variable. In this article we argue in favor of interpreting the CFM as a causal model rather than merely a statistical model, in which common factors are only descriptions of the indicators. When there is sufficient reason to hypothesize that the underlying (...) causal structure of the data is a common cause structure, a causal interpretation of the CFM has several benefits over a merely statistical interpretation of the model. We argue that a causal interpretation conforms with most research questions in which the goal is to explain the correlations between indicators rather than merely summarizing them; a causal interpretation of the factor model legitimizes the focus on shared, rather than unique variance of the indicators; and a causal interpretation of the factor model legitimizes the assumption of local independence. (shrink)

Counterfactual causal interpretations of family genetic effects are appropriate, but neglect an important feature: Provision of unique information about expected outcomes following an independent decision, such as a decision to intervene. Counterfactual causality criteria are unlikely to resolve controversies about behavioral genetic findings; such controversies are likely to continue until counterfactual inferences are translated into interventional hypotheses and designs.

This essay examines the relation between causation and causal explanation. It distinguishes two prominent roles that causes play within the sciences. On the one hand, causes may work as metaphysical posits. From this standpoint, mainstream in contemporary philosophy, causation provides the ‘raw material’ for explanation. On the other hand, causes may be conceived as explanatory postulates, theoretical hypotheses lacking any substantial ontological commitment. This unduly neglected distinction provides the conceptual resources to revisit longstanding philosophical issues, such as overdetermination (...) and causal pluralism. It also inspires a provocative reframing of Russell’s famous, if notoriously elusive, remarks on the nature of causation. (shrink)

Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore (...) this possibility, delineating a path from Mill’s method of agreement to Inference to the Best Explanation (IBE). (shrink)

Falsification no longer is the cornerstone of philosophy of science; but it still looms widely that scientists ought to drop an explanatory hypothesis in view of negative results. We shall argue that, to the contrary, negative empirical results are unable to disqualify causally explanatory hypotheses—not because of the shielding effect of auxiliary assumptions but because of the fact that the causal irrelevance of a factor cannot empirically be established. This perspective is elaborated at a case study taken from (...) the history of plant physiology: the formaldehyde model of photosynthesis, which for about sixty years dominated the field—despite the fact that in these sixty years all the attempts to conclusively demonstrate even the presence of formaldehyde in plants failed. (shrink)

Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and (...) side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study. Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework. Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses. (shrink)

Critics of functional explanations in social science maintain that such explanations are illegitimate unless a mechanism is specified. Others argue that mechanisms are not necessary for causal inference and that functional explanations are a type of causal claim that raise no special difficulties for testing. I show that there is indeed a special problem that confronts testing functional explanations resulting from their connection to second-order causal claims. I explain how mechanisms can resolve this difficulty, but argue that (...) this does not provide support for methodological individualism since it is not necessary that the mechanisms be described in terms of individual interactions. (shrink)