Regression modelQuasi-experimental / causal inference
Heterogeneous Treatment Effect Difference-in-Differences (HTE-DiD)
HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it avoids the biases that arise when a conventional two-way fixed-effects regression is used with staggered adoption or effect heterogeneity, by estimating cohort-and-time-specific average treatment effects that can then be aggregated flexibly.
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Sources
- Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI: 10.1016/j.jeconom.2020.12.001 ↗
- Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI: 10.1016/j.jeconom.2020.09.006 ↗
Related methods
Referenced by
Heterogeneous treatment effect Causal impact analysisHeterogeneous treatment effect Counterfactual impact evaluationHeterogeneous Treatment Effect Interrupted Time SeriesHeterogeneous Treatment Effect Marginal Structural ModelHeterogeneous Treatment Effect Matching EstimatorHeterogeneous treatment effect Panel event studyHeterogeneous treatment effect Placebo testHeterogeneous Treatment Effect Propensity Score MatchingHeterogeneous Treatment Effect Regression Discontinuity DesignHeterogeneous Treatment Effect Sensitivity Analysis for CausalityHeterogeneous Treatment Effect Synthetic Control MethodMachine learning-augmented difference-in-differencesRobust Difference-in-Differences