Observational Causal Methods
Propensity scores, instrumental variables, difference-in-differences, regression discontinuity and synthetic control.
4concepts
58flashcards
31minutes of reading
- 01 Instrumental Variables Using a source of variation that affects treatment but has no other path to the outcome, which recovers a causal effect despite unmeasured confounding, for a subpopulation you cannot identify.
- 02 Propensity Scores and Matching Reducing a high-dimensional covariate vector to a single probability of treatment, which makes balancing tractable but does nothing about the confounders you did not measure.
- 03 Regression Discontinuity and Synthetic Control Two designs that manufacture a credible counterfactual, one from an arbitrary threshold in an assignment rule and one from a weighted combination of untreated units.