Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about ...

This paper is a brief overview and evaluation of current mathematical/statistical causal models, including the structural equation model (SEM), TETRAD, and the graphical model. The efficacy of these approaches will be discussed in the philosophical context of the Duhem-Quine thesis, realism, simplicity, identifiability (testability), empirical adequacy, and probabilistic causality. The emphasis...

We present a sound and complete calculus for causal relevance that uses Pearl’s functional causal models as semantics. The calculus consists of axioms and rules of inference for reasoning about causal relevance relationships. We extend the set of known axioms for causal relevance with new axioms and rules of inference. The axioms are then divided into different sets for reasoning about specific...

We begin by discussing causal independence models and generalize these models to causal interaction models Causal interaction mod els are models that have independent mech anisms where mechanisms can have several causes In addition to introducing several particular types of causal interaction mod els we show how we can apply the Bayesian approach to learning causal interaction mod els obtaining...

Lindquist and Sobel claim that the graphical causal models they call "agnostic" do not imply any counterfactual conditionals. They doubt that "causal effects" can be discovered using graphical causal models typical of SEMs, DCMs, Bayes nets, Granger causal models, etc. Each of these claims is false or exaggerated. They recommend instead that investigators adopt the "potential outcomes" framewor...

Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of hierarchical and/or parallel processes, rather than models of distributions (partially) known to a model builder through some sort of gestalt. As such, Causal Model...

Marginal structural models (MSMs) are becoming increasingly popular as a tool to make causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estim...

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs). This paper describes an alternative new class of causal modelsthe (non-nested) marginal structural models (MSMs). We will then describe a class of semiparametric estimators for the parameters of these new models under a sequential randomization (i.e., ignorability) assu...

We provide a logical representation of Pearl’s structural causal models in the framework of the causal calculus of McCain and Turner (1997) and its first-order generalization by Lifschitz. It will be shown that, under this representation, the nonmonotonic semantics of the causal calculus describes precisely the solutions of the structural equations (the causal worlds of a causal model), while t...