Identifying Causal Effects from Observations (Advanced Data Analysis from an Elementary Point of View)

Reprise of causal effects vs. probabilistic conditioning. "Why think, when you can do the experiment?" Experimentation by controlling everything (Galileo) and by randomizing (Fisher). Confounding and identifiability. The back-door criterion for identifying causal effects: condition on covariates which block undesired paths. The front-door criterion for identification: find isolated and exhaustive causal mechanisms. Deciding how many black boxes to open up. Instrumental variables for identification: finding some exogenous source of variation and tracing its effects. Critique of instrumental variables: vital role of theory, its fragility, consequences of weak instruments. Irremovable confounding: an example with the detection of social influence; the possibility of bounding unidentifiable effects. Summary recommendations for identifying causal effects.

Reading: Notes, chapter 22

Optional reading: Pearl, "Causal Inference in Statistics", sections 3.3--3.5, 4, and 5.1

Advanced Data Analysis from an Elementary Point of View