Observational Causal Methods
Propensity scores, instrumental variables, difference-in-differences, regression discontinuity and synthetic control.
7concepts
137flashcards
56minutes 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 G-Computation and Regression Adjustment Turning an outcome model into a causal effect by predicting every unit's outcome under each treatment and averaging, and why that is not the same as reading a coefficient off the regression.
- 03 Sensitivity Analysis and E-Values Asking how strong an unmeasured confounder would have to be to explain away an observational result, and the three standard ways to quantify that, from Rosenbaum's gamma to the E-value to partial R-squared bounds.
- 04 Double Machine Learning Using arbitrary machine-learning models to adjust for high-dimensional confounders while keeping a root-n, asymptotically normal estimate of the causal parameter, through orthogonal scores and cross-fitting.
- 05 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.
- 06 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.
- 07 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.