A covariate-conditional distributional bridge identifies the ATT under non-monotonic confounding and yields a Neyman-orthogonal, semiparametrically efficient estimator.
A universal difference-in-differences approach for causal inference
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator
A covariate-conditional distributional bridge identifies the ATT under non-monotonic confounding and yields a Neyman-orthogonal, semiparametrically efficient estimator.