I will review concepts, principles, and mathematical tools that were found useful in applications involving causal relationships. The principles are based on structural-model
semantics, in which functional (or counterfactual) relations represent autonomous physical processes. This semantical framework, enriched with a few
ideas from logic and graph theory, gives rise to a complete, coherent, and friendly calculus of causation that unifies the graphical and counterfactual approaches to causation
and resolves long-standing problems in several of the sciences. These include questions of confounding, causal effect estimation,
policy analysis, legal responsibility, effect decomposition, instrumental variables, and the integration of data from diverse studies.
Audio (MP3 File, Podcast Ready)
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