Actual Causation in CP-logic

Given a causal model of some domain and a particular story that has taken place in this domain, the problem of actual causation is deciding which of the possible causes for some effect actually caused it. One of the most influential approaches to this problem has been developed by Halpern and Pearl in the context of structural models. In this paper, I argue that this is actually not the best setting for studying this problem. As an alternative, I offer the probabilistic logic programming language of CP-logic. Unlike structural models, CP-logic incorporates the deviant/default distinction that is generally considered an important aspect of actual causation, and it has an explicitly dynamic semantics, which helps to formalize the stories that serve as input to an actual causation problem

Explaining Actual Causation via Reasoning About Actions and Change

In causality, an actual cause is often defined as an event responsible for bringing about a given outcome in a scenario. In practice, however, identifying this event alone is not always sufficient to provide a satisfactory explanation of how the outcome came to be. In this paper, we motivate this claim using well-known examples and present a novel framework for reasoning more deeply about actual causation. The framework reasons over a scenario and domain knowledge to identify additional events that helped to "set the stage" for the outcome. By leveraging techniques from Reasoning about Actions and Change, the approach supports reasoning over domains in which the evolution of the state of the world over time plays a critical role and enables one to identify and explain the circumstances that led to an outcome of interest. We utilize action language AL for defining the constructs of the framework. This language lends itself quite naturally to an automated translation to Answer Set Programming, using which, reasoning tasks of considerable complexity can be specified and executed. We speculate that a similar approach can also lead to the development of algorithms for our framework

A principled approach to defining actual causation

In this paper we present a new proposal for defining actual causation, i.e., the problem of deciding if one event caused another. We do so within the popular counterfactual tradition initiated by Lewis, which is characterised by attributing a fundamental role to counterfactual dependence. Unlike the currently prominent definitions, our approach proceeds from the ground up: we start from basic principles, and construct a definition of causation that satisfies them. We define the concepts of counterfactual dependence and production, and put forward principles such that dependence is an unnecessary but sufficient condition for causation, whereas production is an insufficient but necessary condition. The resulting definition of causation is a suitable compromise between dependence and production. Every principle is introduced by means of a paradigmatic example of causation. We illustrate some of the benefits of our approach with two examples that have spelled trouble for other accounts. We make all of this formally precise using structural equations, which we extend with a timing over all events

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

Towards a General Framework for Actual Causation Using CP-logic

Abstract. Since Pearl's seminal work on providing a formal language for causality, the subject has garnered a lot of interest among philosophers and researchers in artificial intelligence alike. One of the most debated topics in this context is the notion of actual causation, which concerns itself with specific -as opposed to general -causal claims. The search for a proper formal definition of actual causation has evolved into a controversial debate, that is pervaded with ambiguities and confusion. The goal of our research is twofold. First, we wish to provide a clear way to compare competing definitions. Second, we want to improve upon these definitions so they can be applied to a more diverse range of instances, including non-deterministic ones. To achieve these goals we provide a general, abstract definition of actual causation, formulated in the context of the expressive language of CP-logic (Causal Probabilistic logic). We will then show that three recent definitions by Ned Hall (originally formulated for structural models) and a definition of our own (formulated for CP-logic directly) can be viewed and directly compared as instantiations of this abstract definition, which also allows them to deal with a broader range of examples

A general framework for blaming in component-based systems

International audienceIn component-based safety-critical embedded systems it is crucial to determine the cause(s) of the violation of a safety property, be it to issue a precise alert, to steer the system into a safe state, or to determine liability of component providers. In this paper we present an approach to blame components based on a single execution trace violating a safety property P. The diagnosis relies on counterfactual reasoning (" what would have been the outcome if component C had behaved correctly? ") to distinguish component failures that actually contributed to the outcome from failures that had little or no impact on the violation of P

On probabilistic reasoning of actual causation

Probabilistic actual causation is a theory about actual causal relations in probabilistic scenarios. Compared with general (or type) causal connections, actual (or token, singular) causation involves specific and actual events occurring in a particular time and space. Halpern and Pearl proposed three mathematical definitions on actual causation via structural equation models (or causal models). Fenton-Glynn extended one of their definitions into a probabilistic version by following the probability-raising principle in the tradition of theorizing about probabilistic causation. The basic idea of this principle is that a cause shall raise the probability of its effect. He adopted interventional probabilities to analyse actual causation in causal Bayesian networks. According to Pearl, interventional probabilities of the form P(Y = yX=x) are used to form type-level causal claims, while it is counterfactual probabilities of the form P(Y = yX=x | X = x1, Y = y1) that help us characterize token-level causal relations as the conditionalization part takes actual situations into account; the more facts we condition upon, the closer we come to actual causation. In this dissertation, we modify Fenton-Glynn’s probabilistic definition of actual causation in probabilistic causal models by employing counterfactual probability raising instead of his interventional probability raising. Our new definitions PAC and PAC’ are capable of dealing with a number of probabilistic versions of causal examples in which Fenton-Glynn’s definition fails, such as voting, overlapping, trumping, etc. Alternatively, we can exploit elaborate and plausible counterfactual definitions of actual causation, once counterfactuals are interpreted probabilistically, essentially we turn the deterministic theories of actual causation into their indeterministic versions. That can be seen as the second or new approach to defining probabilistic actual causation compared with the traditional straightforward probability-raising approach. In order to realize this idea, we propose a probabilistic semantics for causal counterfactuals in probabilistic causal models using counterfactual probabilities. Causal counterfactuals or interventional sentences have the form [X ←x]Y = y with the meaning “if X is manipulated to take value x, Y has value y.” Causation definitions in the causal modeling framework take interventional sentences as counterfactuals. Our proposed semantics for [X ← x]Y = y is its corresponding counterfactual probability being very high (e.g. P(Y = yX=x | e) = 1). For this semantics, we provide a sound and complete axiomatization. Based on this logical result, Halpern’s latest definition of actual causation can be translated as a probabilistic version by interpreting interventional statements in it with our semantics. The difference between definitions from this approach and the traditional one turns out to be the extent of probability increase, namely, slight (traditional approach) or significant (new approach) probability raising. We compare the logical properties of our conception PAC of probabilistic actual causation and the new indeterministic version of Halpern’s latest actual causation by treating these two definitions as the semantics for causal conditionals

Counterfactual dependency and actual causation in CP-logic and structural models: A comparison

The solution to the problem of actual causation - i.e. determining what caused an effect in a specific scenario - put forward by Halpern and Pearl recently has received a lot of attention. It forms the basis for many other approaches within the dominant tradition of counterfactual theories of causation. However, their solu- tion runs into a number of difficulties for a certain type of examples exhibiting so- called switching causation and early preemption. We discuss these in the light of the core concept of counterfactual dependency, and offer a comparison with the recent definition of actual causation formulated in CP-logic. We argue both that for this type of examples the CP-logic definition provides better anwers, and that it does more justice to the fundamental intuitions underlying counterfactual dependency.status: publishe