Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural (...) assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial. (shrink)

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

Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the (...) class='Hi'>intervention in fact did, is difficult to reliably estimate. A further complication is that the causal findings in psychology are typically made at the population level, and such findings do not allow inferences to individual-level causal relationships. I also discuss the implications of these problems for research, as well as various ways of addressing them, such as focusing more on the discovery of robust but non-causal patterns. (shrink)

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

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal (...) class='Hi'>intervention(s) depends heavily on the particular experimental setup and the assumptions that can be made. ‡The first author is funded by the Causal Learning Collaborative Initiative supported by the James S. McDonnell Foundation. Many aspects of this paper were inspired by discussions with members of the collaborative. †To contact the authors, please write to: Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] and [email protected]. (shrink)

In psychiatry, pharmacological drugs play an important experimental role in attempts to identify the neurobiological causes of mental disorders. Besides being developed in applied contexts as potential treatments for patients with mental disorders, pharmacological drugs play a crucial role in research contexts as experimental instruments that facilitate the formulation and revision of neurobiological theories of psychopathology. This paper examines the various epistemic functions that pharmacological drugs serve in the discovery, refinement, testing, and elaboration of neurobiological theories of mental disorders. I (...) articulate this thesis with reference to the history of antipsychotic drugs and the evolution of the dopamine hypothesis of schizophrenia in the second half of the twentieth century. I argue that interventions with psychiatric patients through the medium of antipsychotic drugs provide researchers with information and evidence about the neurobiological causes of schizophrenia. This analysis highlights the importance of pharmacological drugs as research tools in the generation of psychiatric knowledge and the dynamic relationship between practical and theoretical contexts in psychiatry. (shrink)

Glennan appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality . I argue that Glennan’s manoeuvre fails. The appeal to interventions is not able to address the ontological regress, and it blocks the explanatory regress only at the cost of making the account inapplicable to non-modular mechanisms. I offer a solution to the explanatory regress that makes use of dynamic Bayesian networks. My argument is illustrated by a case study from systems biology, (...) namely, the mechanism for the irreversibility of apoptosis. I conclude by pointing out the implications of my argument for Glennan’s mechanistic account of causality and, more generally, for accounts of mechanistic explanation based on interventions. 1 Introduction2 Glennan’s Account of Causality3 Objections to Glennan’s Account4 Glennan’s Replies5 Ontological Symmetry?6 Explanatory Symmetry?7 A Solution to the Explanatory Regress8 The Prospects of Glennan’s Account. (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 application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent with (...) one another. In Experiment 1, children aged between 4 and 8 years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children’s causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. (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)

The past century has witnessed numerous methodological innovations in probabilistic and statistical methods of causal inference (e.g., the graphical modelling and the potential outcomes frameworks, as introduced in Chapter 1). These innovations have not only enhanced the methodologies by which scientists across diverse domains make causal inference, but they have also made a profound impact on the way philosophers think about causation. The philosophical issues discussed in this thesis are stimulated and inspired by these methodological innovations. Chapter 2 (...) addresses the question of how the holding of screening-off conditions for a causal model depends on the choice of variables. As bridge principles between probability and causation, screening-off conditions (especially the Causal Markov Condition) play a key role in causal inference. However, it has been known that these conditions may fail due to poor variable choice. My aim in this chapter is to further examine those constraints on variable choice that are deemed necessary for the satisfaction of screening-off conditions. The idea of a well-defined (hypothetical) intervention is also crucial for reliable causal inference. Chapter 3 explores the question of when interventions invoked in causal inference are “well-defined” or unambiguous, and how this requirement constrains the choice of cause-variables. I propose that an intervention is well-defined just in case the effect of interest is well-defined (under ideal interventions), and that the intervention can serve as a suitable means to identify that effect. Based on this proposal, several distinct types of ambiguous interventions are identified. Methodological progress in causal inference also poses the following question: Can such progress shed light on the ontology of causation? My answer is yes. In Chapter 4, I develop a Carnapian-pragmatist approach to the ontology of causation as an alternative to existing metaphysical approaches. I argue that, compared to traditional metaphysics, the pragmatist approach provides a superior picture of how the ontology and methodology of causation interact with each other in scientific practice. I conclude in Chapter 5 that the thing we call “causation” consists in both the right worldly infrastructure (e.g., screening-off patterns and possibilities for interventions) and appropriate ways of framing this infrastructure. (shrink)

The aim of this paper is to introduce the instrumental variables technique to the discussion about causal inference in econometrics. I show that it may lead to causally incorrect conclusions unless some fairly strong causal background assumptions are made, assumptions which are usually left implicit by econometricians. These assumptions are very similar to, albeit not identical with, James Woodward's definition of an ‘intervention’. I discuss similarities and differences of the two points of view and argue that—understood as (...) a practical method of causal inference—the set presented here is superior. (shrink)

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same (...) time changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized. (shrink)

Two strains of interventionist responses to the causal exclusion argument are reviewed and critically assessed. On the one hand, one can argue that manipulating supervenient mental states is an effective strategy for manipulating the subvenient physical states, and hence should count as genuine causes to the subvenient physical states. But unless the supervenient and subvenient states manifest some difference in their manipulability conditions, there is no reason to treat them as distinct, which in turn goes against the basic assumption (...) of nonreductive physicalism. On the other hand, one can preserve the distinction between the two by introducing asymmetric manipulability conditions that the supervenience thesis entails. But this response can be used to argue that mental causes never have physical effects. However, this argumentation can also be used to show that mental causes can have mental effects. (shrink)

In the so-called “Spin-Echo Experiments” the behaviour of a spin’s system seems to violate the second law of thermodynamics. For this reason the “Spin-Echo Experiments” are considered of particular interest for the Foundations of Physics. Interventionists have provided a classical explanation (Blatt 1959; Ridderbos & Redhead 1998) and a quantum-based explanation (Hemmo & Shenker 2005) of these experiments. Here both interventionist explanations are assessed by means of the Manipulability Theory of Causal Explanation (Woodward 2003). It is argued that interventionism (...) would gain explanatory depth by providing functional relations and predicting relaxation times. En los llamados “experimentos espín-eco” el comportamiento del sistema de espines parece violar la segunda ley de la termodinámica. Por esta razón, los “experimentos espín-eco” son considerados de gran interés en Fundamentos de la Física. Los intervencionistas han ofrecido explicaciones clásicas (Blatt 1959; Ridderbos & Redhead 1998) y explicaciones basadas en mecánica cuántica (Hemmo & Shenker 2005) de dichos experimentos. Aquí ambas explicaciones intervencionistas son analizadas mediante la Teoría Manipulabilista de las Explicaciones Causales (Woodward 2003). Se argumenta que el intervencionismo ganaría profundidad explicativa si proporcionara relaciones funcionales y realizara predicciones de los tiempos de relajación. (shrink)

In the so-called "Spin-Echo Experiments" the behaviour of a spin's system seems to violate the second law of thermodynamics. For this reason the "Spin-Echo Experiments" are considered of particular interest for the Foundations of Physics. Interventionists have provided a classical explanation (Blatt, 1959; Ridderbos & Redhead, 1998) and a quantum-based explanation (Hemmo & Shenker, 2005) of these experiments. Here both interventionist explanations are assessed by means of the Manipulability Theory of Causal Explanation (Woodward, 2003). It is argued that interventionism (...) would gain explanatory depth by providing functional relations and predicting relaxation times. (shrink)

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi‐coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new (...) model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. (shrink)

There has been much debate among scientists and philosophers about what it means for interventions invoked in causal inference to be “well-defined” and how considerations of this sort should constrain the choice of causal variables. In this paper, I propose that an intervention is well-defined just in case the effect of interest is well-defined, and that the intervention can serve as a suitable means to identify that effect. Based on this proposal, I identify several types of (...) ambiguous intervention. Implications for variable choice are discussed using case studies drawn from the sciences. (shrink)

We investigate how people use causal knowledge to design interventions to affect the outcomes of causal systems. We propose that in addition to using content or mechanism knowledge to evaluate the effectiveness of interventions, people are also influenced by the abstract structural properties of a causal system. In particular, we investigated two factors that influence whether people tend to intervene proximally (on the immediate cause of an outcome of interest) or distally (on the root cause of a (...) chain leading to the outcome). We presented people with causal chains describing a variety of real-world and artificial causal systems and asked them where they would intervene to affect the outcome. In Experiment 1, participants who were asked to choose the best long-term intervention intervened more distally than participants asked to choose the best short-term intervention. In Experiment 2, participants presented with a branching structure in which there were two distinct causal pathways from the root cause to the outcome were more likely to intervene on the root cause than participants presented with only one of the pathways. Our findings demonstrate two ways in which people integrate content knowledge and knowledge of a system’s causal structure to design effective interventions. (shrink)

Detecting and correcting execution errors is crucial for safe and efficient goal-directed behavior. Despite intensive investigations on error processing, the cognitive foundations of this process remain unclear. Based on the presumed relation between executive attention and error processing, we implemented a seven-day EA intervention by adopting the Posner cueing paradigm to test the potential causal link from EA to error processing in healthy adults. The experimental group was trained on the Posner cueing paradigm, with a ratio of invalid (...) cue trials to valid cue trials of 5:1 and a corresponding ratio of 1:1 in the active control group. We found that the EA intervention improved EA across intervention sessions. Critically, after the EA intervention, the HEA group showed that post-error accuracy was restored to the same level as the post-correct accuracy. However, post-error slowing and the flanker effect were not modulated by the EA intervention. Furthermore, we observed that the changes in the accuracy of VC trials positively predicted the changes in PEA and that the two groups were classified according to the changes in PEA with a 61.3% accuracy. Based on these results, we propose that EA causally drives error processing. And the capabilities of the “actively catch” more attention resources and the automatic mismatch processing developed after EA intervention is transferable to error processing, thereby directly resulting in the gains in post-error adjustments. Our work informs the potential cognitive mechanisms underlying this causal link. (shrink)

BACKGROUND: Representing is about theories and theory formation. Philosophy of science has a long-standing interest in representing. At least since Ian Hacking's modern classic Representing and Intervening analytical philosophers have struggled to combine that interest with a study of the roles of intervention studies. With few exceptions this focus of philosophy of science has been on physics and other natural sciences. In particular, there have been few attempts to analyse the use of the notion of intervention in other (...) disciplines where intervention studies are important, such as in nursing research. One unintended consequence of this is that the relations between representing and intervening tend to be less understood outside the natural sciences. OBJECTIVES AND DESIGN: This article highlights a number of possible topics on which nursing science and analytic philosophy of science can fruitfully interact. The basic idea is simple. Building on a characterisation of interventions in terms of what is intervened on and with respect to what, we suggest that interventions in nursing research typically are a blend of varieties belonging to the three dimensions of agency, epistemology, and ontology. The details of the blend determine the relation of the particular intervention study to traditional representational categories such as inductivism and hypothetico-deductive method, and have a bearing on its explanatory power and other more theory independent features of research as well. The framework we suggest should be relevant for nurse researchers who want to adopt a more general and analytically entrenched perspective on representing and intervening than the methodological boundaries in nursing research typically allow. (shrink)

Word count (excluding abstract and references): 2,498 words. Address for correspondence: T. Kushnir, Psychology Department, University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650. Phone: 510-205-9847. Fax: 510-642- 5293. E-mail: [email protected].

Semmelweis’s work predates the discovery of the power of randomization in medicine by almost a century. Although Semmelweis would not have consciously used a randomized controlled trial (RCT), some features of his material—the allocation of patients to the first and second clinics—did involve what was in fact a randomization, though this was not realised at the time. This article begins by explaining why Semmelweis’s methodology, nevertheless, did not amount to the use of a RCT. It then shows why it is (...) descriptively and normatively interesting to compare what he did with the modern approach using RCTs. The argumentation centres on causal inferences and the contrast between Semmelweis’s causal concept and that deployed by many advocates of RCTs. It is argued that Semmelweis’s approach has implications for matters of explanation and medical practice. (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)

This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of (...) public health interventions. While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims, but associations and classification schema are more plausibly discovered in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. (...) We then consider three key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...) impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

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

This paper, like its companion explores some ways in which, on the one hand, normative theorizing about causation and causal reasoning and, on the other, empirical psychological investigations into causal cognition can be mutually illuminating. The topics considered include the connection between causal claims and claims about the outcomes of interventions and the various ways that invariance claims figure in causal judgment.

This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning.

Causal exclusion arguments are taken to threaten the autonomy of the special sciences, and the causal efficacy of mental properties. A recent line of response to these arguments has appealed to “independently plausible” and “well grounded” theories of causation to rebut key premises. In this paper I consider two papers which proceed in this vein and show that they share a common feature: they both require causes to be proportional to their effects. I argue that this feature is (...) a bug, and one that generalises: any attempt to rescue the autonomy of the special sciences, or the efficacy of the mental, from exclusion worries had better not look to proportionality for help. (shrink)

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning (...) via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (shrink)

Analyzing causation in terms of Woodward's interventionist theory and describing the structure of the world in terms of causal powers are usually regarded as quite different projects in contemporary philosophy. Interventionists aim to give an account of how causal relations can be empirically discovered and described, without committing themselves to views about what causation really is. Causal powers theorists engage in precisely the latter project, aiming to describe the metaphysical structure of the world. In this paper, I (...) argue that interventionism can benefit from incorporating considerations about causal powers. I describe a previously undiscussed problem for Woodward's definition of an intervention variable that arises when interventionist causal models include disjunctive variables. This problem is solved not by excluding disjunctive (or logically compound) variables per se, but by excluding variables whose values represent disjunctions of too different causal powers. This suggests that interventionism and causal powers theories may be less distant from each other than is often assumed. (shrink)

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

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

A growing number of philosophers are bringing interventionism into the field of supervenient causation. Many argue that interventionist supervenient causation is exempted from the fixability condition. However, this approach looks ad hoc, inconsistent with the general interventionist requirement on fixation. Moreover, it leads to false judgments about the causal efficacy of supervenient/subvenient properties. This article aims to develop a novel interventionist account of supervenient causation that respects the fixability requirement. The treatment of intervention and fixation that I propose (...) can accommodate some theoretical constraints on causation and deliver correct causal verdicts in classic examples. It is also worth noting that this interventionist account offers a promising defense of mental causation without postulating mental-physical overdetermination. (shrink)

We further develop the mathematical theory of causal interventions, extending earlier results of Korb, Twardy, Handfield, & Oppy, (2005) and Spirtes, Glymour, Scheines (2000). Some of the skepticism surrounding causal discovery has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (cf. Cartwright, (2001). Our results show that experimental data, together with some plausible assumptions, can reduce the (...) space of viable explanatory causal models to one. (shrink)

Graphical AbstractMicrobiome research attributes to whole microbiomes a causal role in the occurrence of different health outcomes. I argue, following some distinctions about causal relationships and explanations made within a philosophical account of causation, the “interventionist account,” that such claims need more scrutiny.

"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)

Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead (...) use temporal and probabilistic information to infer exactly when X is causally relevant to Y in each of the senses captured by Woodward’s interventionist treatment. Then, I consider the upshot of these findings for causal decision theory, and argue that the commonsense assumption is especially powerful when an agent seeks to determine whether so-called “dominance reasoning” is applicable. (shrink)

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I (...) spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals. (shrink)