Introduces BGATT estimand and influence-function-based estimators for covariate-balanced group treatment effect heterogeneity in DiD under conditional parallel trends, with ML nuisance estimation and asymptotic normality.
Difference in Differences with Time-Varying Covariates
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Graphical criteria enable formal covariate selection for conditional parallel trends in DiD, revealing conflicts with unconditional assumptions, useful roles for time-invariant covariates, and misalignment issues in popular estimators.
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Group-Level Treatment Effect Heterogeneity in Difference-in-Differences: A Balanced Approach
Introduces BGATT estimand and influence-function-based estimators for covariate-balanced group treatment effect heterogeneity in DiD under conditional parallel trends, with ML nuisance estimation and asymptotic normality.
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A formal approach to variable selection in difference-in-differences
Graphical criteria enable formal covariate selection for conditional parallel trends in DiD, revealing conflicts with unconditional assumptions, useful roles for time-invariant covariates, and misalignment issues in popular estimators.