Cause Of Action Research Articles

Reasoning about actual causation in reversible and irreversible causal structures.

This article investigates people's judgments of actual causation in the context of a previously neglected property of causal structures-their reversibility, that is, whether an effect persists or returns to its original state if its causes are removed. Causal reversibility, and its potential impact on causal judgment, was recently analyzed theoretically by Ross and Woodward (2022). They hypothesized that reversibility might affect people's evaluation of causes in late-preemption scenarios. The typical finding in preemption scenarios is that events happening earlier are considered to be actual causes, while events happening later are regarded as noncauses. The hypothesis is that this robust intuition depends on causal reversibility and that in reversible structures later events are regarded as actual causes. Across three main experiments and one supplementary study (N = 590), it is shown that reversibility has the predicted effect: later causes are perceived to make an actual causal contribution to the effect. It is also shown that Henne et al. (2023), in a first study, did not find evidence for Ross and Woodward's hypothesis because they did not test whether people regard later causes in preemption-like sequences of reversible structures as maintainers and not as triggers of their effect. Because they used test questions that asked explicitly for triggering rather than maintaining or were at least ambiguous, their results seemed to show that people think that later events have no causal impact. Maintaining is a relevant causal concept deserving more attention in both philosophical theories and psychological studies on causal cognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Broken brakes and dreaming drivers: the heuristic value of causal models in the law

Recently, there has been an increased interest in employing model-based definitions of actual causation in legal inquiry. The formal precision of such approaches promises to be an improvement over more traditional approaches. Yet model-based approaches are viable only if suitable models of legal cases can be provided, and providing such models is sometimes difficult. I argue that causal-model-based definitions benefit legal inquiry in an indirect way. They make explicit the causal assumptions that need to be made plausible to defend a particular claim of actual causation. My argument concerns the analysis of legal cases involving a combination of double prevention and causal redundancy. I show that discussions among legal theorists about such cases sometimes suffer from ambiguous assumptions about the causal structure. My account illustrates that causal models can act as a heuristic tool for clarifying such assumptions, and that causal models provide a framework for more accurate analyses of legal cases involving complex causal structure.

Causal Bayes Nets and Token-Causation: Closing the Gap between Token-Level and Type-Level

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

The causal structure of Frankfurt‐ and PAP‐style cases

AbstractFrankfurt‐style cases suggest that an agent's moral responsibility for an action supervenes on the causal history of that action—at least when epistemic considerations are held constant. However, PAP‐style cases suggest that moral responsibility does not supervene on causal history, for judgments concerning an agent's responsibility for an action are also sensitive to the presence of alternative—and causally idle—possibilities. I appeal to the causal modeling tradition and the definitions of actual causation that derive therefrom in an attempt to resolve this contradiction. I show that even the weakest definitions of actual causation proposed in the literature establish that some PAP‐style cases constitute genuine counterexamples to the supervenience thesis. I consider several responses to these counterexamples on behalf of the defenders of supervenience and show that they fail. Our best current thinking on causation thus appears to be inconsistent with an intuitive and widely held claim concerning the nature of moral responsibility.

Poisoned Babies, Shot Fathers, and Ruined Experiments: Experimental Evidence in Favor of the Compositionality Constraint of Actual Causation

AbstractLivengood and Sytsma (2020) challenge the compositionality constraint of actual causation (CCAC), according to which each intermediary of a causal chain is an effect of its predecessor and a cause of its successor link. In several studies, they find support for their hypothesis that the CCAC is not in accordance with the ordinary causal attributions of laypeople. We argue that there are three interrelated problems in their studies’ design that we call the causality-responsibility confusion (CRC), the intermediary-ontology confusion (IOC), and the cause-end questioning (CEQ). Avoiding the CRC, the IOC, and the CEQ leads to strong empirical support for the CCAC.

Actual Causation and the Challenge of Purpose

This paper explores the prospects of employing a functional approach in order to improve our concept of actual causation. Claims of actual causation play an important role for a variety of purposes. In particular, they are relevant for identifying suitable targets for intervention, and they are relevant for our practices of ascribing responsibility. I argue that this gives rise to the challenge of purpose. The challenge of purpose arises when different goals demand adjustments of the concept that pull in opposing directions. More specifically, I argue that a common distinction between certain kinds of preempted and preempting factors is difficult to motivate from an interventionist viewpoint. This indicates that an appropriately revised concept of actual causation would not distinguish between these two kinds of factors. From the viewpoint of retributivist responsibility, however, the distinction between preempted and preempting factors sometimes is important, which indicates that the distinction should be retained.

The Nonidentity Problem is an Artifact of Faulty Causal Reasoning

This paper argues that the nonidentity problem is really an artifact of faulty causal reasoning. In order to solve the nonidentity problem, we must determine that an agent causes a loss of happiness to another agent by means of an action that also causes the victim to exist. Woodward’s (2003) test for actual causation yields just this result. Equally crucial to solving the problem is the recognition that harms must be intentional and that intentionality is a function of norm-violation; this latter position is defended by Holton (2010). That the solution proposed is adequate is defended on the grounds that it yields intuitively correct judgments for a host of imaginary scenarios contemplated by theorists focused on determining what should count as harms.

Answers at Gunpoint: On Livengood and Sytsma’s Revolver Case

AbstractJonathan Livengood and Justin Sytsma have published a series of studies on “Actual Causation and Compositionality,” in which they investigate causal attributions of laypeople. We use one of their vignettes to follow up on their research. Our findings cast doubt on their conclusion that ordinary causal attributions tend to violate the compositionality constraint if one looks at cases in which someone is responsible for an effect by way of an intermediary that does not share in the responsibility.

Correction to: Causal Sufficiency and Actual Causation

Correction to: Causal Sufficiency and Actual Causation

Causal Sufficiency and Actual Causation

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.

The Counterfactual NESS Definition of Causation

Beckers & Vennekens recently proposed a definition of actual causation that is based on certain plausible principles, thereby allowing the debate on causation to shift away from its heavy focus on examples towards a more systematic analysis. This paper contributes to that analysis in two ways. First, I show that their definition is in fact a formalization of Wright’s famous NESS definition of causation combined with a counterfactual difference-making condition. This means that their definition integrates two highly influential approaches to causation that are claimed to stand in opposition to each other. Second, I modify their definition to offer a substantial improvement: I weaken their difference-making condition in such a way that it avoids their problematic analysis of cases of preemption. The resulting Counterfactual NESS definition of causation forms a natural compromise between counterfactual approaches and the NESS approach.

Advances in our understanding of canine atopic dermatitis.

Canine atopic dermatitis (cAD) is a genetically inherited clinical syndrome that encompasses a diversity of mechanisms and can have a variety of triggers. Development of clinical disease is the result of genetic factors and environmental conditions, which shape the resulting immunological response. Clinical disease becomes evident once a threshold of inflammatory response is achieved. Skin barrier impairment plays a role in promoting cutaneous dysbiosis and increased allergen penetration. Keratinocytes shape the response of dendritic cells and subsequent lymphocytic response. Thymic stromal lymphopoietin is one of the links between the damaged skin barrier and the modulation of a T-helper (Th)2 response. It is still unclear whether mutations in skin barrier genes exist in atopic dogs, as they do in humans, or whether the observed alterations are purely secondary to inflammation. A dysregulated immune response with increased Th2, Th17 and CD4+ CD25+ regulatory T cells has been reported. A variety of cytokines [interleukin(IL)-31, IL-34, Macrophage migration inhibitory factor] are proposed as potential biomarkers and treatment targets because they are increased in the serum of atopic dogs when compared to controls, although a correlation between serum levels of these factors and severity of disease is not always present. The main issue with many published studies is that atopic dogs are always only compared to normal controls. Thus, it is unclear whether the changes that we find are truly a signature of cAD or merely a manifestation of nonspecific broad inflammatory responses. Studies considering comparison with other inflammatory diseases different from cAD are urgently needed to correctly identify what is specific to this complicated syndrome.

A Ramsey Test Analysis of Causation for Causal Models

We aim to devise a Ramsey test analysis of actual causation. Our method is to define a strengthened Ramsey test for causal models. Unlike the accounts of Halpern and Pearl ([2005]) and Halpern ([2015]), the resulting analysis deals satisfactorily with both overdetermination and conjunctive scenarios.

Norms in Counterfactual Selection

AbstractIn the hopes of finding supporting evidence for various accounts of actual causation, many philosophers have recently turned to psychological findings about the influence of norms on counterfactual cognition. Surprisingly little philosophical attention has been paid, however, to the question of why considerations of normality should be relevant to counterfactual cognition to begin with. In this paper, I follow two aims. First, against the methodology of two prominent psychological accounts, I argue for a functional approach to understanding the selectivity of counterfactual cognition. Second, I take some steps towards a systematic analysis by providing a qualitative, decision‐theoretic account of one important function of counterfactual thinking, namely, inferring, in the face of undesirable outcomes, corrective policies that prevent the occurrence of similar outcomes in future circumstances. I make a case for employing this analysis by (a) showing its value for assessing the rationality of imagination‐driven counterfactual generation, (b) highlighting its use for making sense of practices in history and policy analysis where counterfactual selection plays a central role, and (c) demonstrating its diagnostic value for identifying areas where counterfactual generation may lead us into epistemic and ethical troubles.

Actual Causation and Compositionality

Many theories of actual causation implicitly endorse the claim that if c is an actual cause of e, then either c causes e directly or every intermediary by which c indirectly causes e is itself both an actual cause of e and also an actual effect of c. We think this compositionality constraint is plausible. However, as we show, it is not always satisfied by the causal attributions ordinary people make. We conclude by considering what philosophers working on causation should do when the deliverances of their theories diverge from what ordinary people say.

Causality & Control Flow

Causality has been the issue of philosophic debate since Hippocrates. It is used in formal verification and testing, e.g., to explain counterexamples or construct fault trees. Recent work defines actual causation in terms of Pearl's causality framework, but most definitions brought forward so far struggle with examples where one event preempts another one. A key point to capturing such examples in the context of programs or distributed systems is a sound treatment of control flow. We discuss how causal models should incorporate control flow and discover that much of what Pearl/Halpern's notion of contingencies tries to capture is captured better by an explicit modelling of the control flow in terms of structural equations and an arguably simpler definition. Inspired by causality notions in the security domain, we bring forward a definition of causality that takes these control-variables into account. This definition provides a clear picture of the interaction between control flow and causality and captures these notoriously difficult preemption examples without secondary concepts. We give convincing results on a benchmark of 34 examples from the literature.

What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks.

Actual causation is concerned with the question: “What caused what?” Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system’s causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the “what caused what?” question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation, that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements based on system interventions and partitions, which considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.

Causation in terms of production

In this paper, we analyse actual causation in terms of production. The latter concept is made precise by a strengthened Ramsey Test semantics of conditionals: \(A \gg C\) iff, after suspending judgement about A and C, C is believed in the course of assuming A. This test allows us to (epistemically) verify or falsify that an event brings about another event. Complementing the concept of production by a weak condition of difference-making gives rise to a full-fledged analysis of causation.

Arguing about causes in law: a semi-formal framework for causal arguments

Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation (but-for, NESS, ‘actual causation’) to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law.

Processes, pre-emption and further problems

In this paper I will argue that what makes our ordinary judgements about token causation (‘actual causation’) true can be explicated in terms of interferences into quasi-inertial processes. These interferences and quasi-inertial processes can in turn be fully explicated in scientific terms. In this sense the account presented here is reductive. I will furthermore argue that this version of a process-theory of causation can deal with the traditional problems that process theories have to face, such as the problem of misconnection and the problem of disconnection (Dowe in The Oxford handbook of causation, Oxford University Press, Oxford, 2009) as well as with a problem concerning the mis-classification of pre-emption cases (Paul and Hall in Causation: a user’s guide, Oxford University Press, Oxford, 2013).