Observational Causal Methods
4concepts
29flashcards
31minutes of reading
- 01 Difference-in-Differences and Parallel Trends Using a control group's change over time to estimate what the treated group's change would have been, and the untestable assumption that carries the entire argument.
- 02 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.
- 03 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.
- 04 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.