Causal Data Science

Catalog description

Potential outcomes, randomized experiments, observational studies, effect modification, interaction, causal directed acyclic graphs, Judea Pearl’s Theory of Causality, adjustment for confounding, selection bias, measurement bias, standardization, difference-in-differences, the front door method, instrumental variables, propensity-score methods and targeted learning.