I will review concepts, principles, and mathematical tools that were found useful in applications involving causal and counterfactual relationships. This semantical framework, enriched with a few ideas from logic and graph theory, gives rise to a complete, ...

Keywords: causal inference

Recent advances in causal reasoning have given rise to a computation model that emulates the process by which humans generate, evaluate and distinguish counterfactual sentences. Though compatible with the "possible worlds" account, this model enjoys ...

Keywords: 68T30, 68T37, Causal reasoning, Conditional logic, Counterfactuals

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications ...

Many applications of causal analysis call for assessing, retrospectively, the effect of with-holding an action that has in fact been implemented. This counterfactual quantity, sometimes called "effect of treatment on the treated," (ETT) have been used ...

The construction of causal graphs from nonexperimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph. These constraints are of two types: conditional independencies and ...

We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from ...

Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this article, we propose ...

We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network of unknown structure, in which some of the variables remain unmeasured. We show that such ...