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Abstract:
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In this tutorial, I will introduce a few basic principles and simple mathematical tools that were found useful in solving most problems involving causal inference. The principles are based on nonparametric structural equation models, a natural generalization of those used by econometricians in the 1950--60s, yet cast in new mathematical underpinnings. This semantical framework, enriched with a few ideas from logic and graph theory, gives rise to a friendly calculus of causes and counterfactuals that unifies all existing approaches to causation and enable rank and file researchers to handle long-standing problems in several of the sciences. These include questions of confounding, causal effect estimation, covariate selection, policy analysis, legal responsibility, effect decomposition, instrumental variables, and the integration of data from diverse studies.
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