DSC 245Introduction to Causal Inference
Causal versus predictive inference, potential outcomes and randomized experiments (A/B testing), structural causal models (interventions, counterfactuals, causal diagram, do-operator, d-separation), causal structure learning, identification of causal effect, estimation of causal effect, causal discovery and inference in presence of distribution shifts, selection bias, hidden confounders, cycles, nonlinear causal mechanisms, missing values, causal representation learning.