Graphical Causal Models (Advanced Data Analysis from an Elementary Point of View)

Probabilistic prediction is about passively selecting a sub-ensemble, leaving all the mechanisms in place, and seeing what turns up after applying that filter. Causal prediction is about actively producing a new ensemble, and seeing what would happen if something were to change ("counterfactuals"). Graphical causal models are a way of reasoning about causal prediction; their algebraic counterparts are structural equation models (generally nonlinear and non-Gaussian). The causal Markov property. Faithfulness. Performing causal prediction by "surgery" on causal graphical models. The d-separation criterion. Path diagram rules for linear models.

Reading: Notes, chapter 21

Optional reading: Cox and Donnelly, chapter 9; Pearl, "Causal Inference in Statistics", section 1, 2, and 3 through 3.2

Advanced Data Analysis from an Elementary Point of View