Actual causation and the art of modeling

We look more carefully at the modeling of causality using structural equations. It is clear that the structural equations can have a major impact on the conclusions we draw about causality. In particular, the choice of variables and their values can also have a significant impact on causality. These choices are, to some extent, subjective. We consider what counts as an appropriate choice. More generally, we consider what makes a model an appropriate model, especially if we want to take defaults into account, as was argued is necessary in recent work.Comment: In Heuristics, Probability and Causality: A Tribute to Judea Pearl (editors, R. Dechter, H. Geffner, and J. Y. Halpern), College Publications, 2010, pp. 383-40

Counterfactual Causality from First Principles?

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277

Causal Responsibility and Counterfactuals.

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions

Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.Comment: Under consideration for publication in Theory and Practice of Logic Programmin

Is There High-Level Causation?

The discovery of high-level causal relations seems a central activity of the special sciences. Those same sciences are less successful in formulating strict laws. If causation must be underwritten by strict laws, we are faced with a puzzle (first noticed by Donald Davidson), which might be dubbed the 'no strict laws' problem for high-level causation. Attempts have been made to dissolve this problem by showing that leading theories of causation do not in fact require that causation be underwritten by strict laws. But this conclusion has been too hastily drawn. Philosophers have tended to equate non-strict laws with ceteris paribus laws. I argue that there is another category of non-strict law that has often not been properly distinguished: namely, (what I will call) minutiae rectus laws. If, as it appears, many special science laws are minutiae rectus laws, then this poses a problem for their ability to underwrite causal relations in a way that their typically ceteris paribus nature does not. I argue that the best prospect for resolving the resurgent 'no strict laws' problem is to argue that special science laws are in fact typically probabilistic (and thus able to support probabilistic causation), rather than being minutiae rectus laws

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

In legal argumentation and liability attribution, disputes over causes play a central role. Legal discussions about causation often have difficulty with cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. We first assess three theories of causation. Then we introduce a semi-formal framework to model causal arguments using both strict and defeasible rules. We apply the framework to the Althen vaccine injury case. Wrapping up the paper, we motivate a causal argumentation framework and propose to integrate current theories of causation