A modal language called hypothetical logic is presented in which the semantics of counterfactuals is captured by a new syntactic version of belief updating that incorporates reason maintenance; the latter is claimed necessary for a theory of counterfactuals. The logic is then applied to problems in causation. Unlike other systems for information change, events and time in this framework are rep...

Semantics of counterfactuals is normally developed according to the principle of similarity, and the key point is to specify the notion of relative similarity. It can be seen in specific examples that causation plays a crucial role in determining the independence of particular facts and thus also in the measure of relative similarity. Counterfactual account of causation is briefly reviewed. Som...

According to common judicial standard, judgment in favor of plaintiff should be made if and only if it is “more probable than not” that the defendant’s action was the cause for the plaintiff’s damage (or death). This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or sufficient cause (or both) of another event y. T...

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does n...

A quite popular approach to solving the Causal Exclusion Problem is to adopt a counterfactual theory of causation. In this paper, I distinguish three versions of the Causal Exclusion Argument. I argue that the counterfactualist approach can block the first two exclusion arguments, because the Causal Inheritance Principle and the Upward Causation Principle upon which the two arguments are based ...

2 Mutual information 3 Mutual information . . . . . . . . . . . . . . . . 3 Multivariate mutual information . . . . . . . . . 3 Interaction information . . . . . . . . . . . . . . 4 Conditional mutual information . . . . . . . . . 5 Binding information . . . . . . . . . . . . . . . . 6 Residual entropy . . . . . . . . . . . . . . . . . . 6 Total correlation . . . . . . . . . . . . . . . . . . 6...

Causation looms large in legal and moral reasoning. People construct causal models of the social and physical world to understand what has happened, how and why, and to allocate responsibility and blame. This chapter explores people’s commonsense notion of causation, and shows how it underpins moral and legal judgments. As a guiding framework it uses the causal model framework (Pearl, 2000) roo...

Causal judgments are well-known to be sensitive to violations of both prescriptive moral and descriptive statistical norms. There is ongoing discussion as to whether both effects are best explained through changes in the relevance of counterfactual possibilities, or if moral norm violations should be independently explained through a potential polysemy whereby ‘cause’ may simply mean ‘is morall...