Econometrics Seminar Series.  Dr Liyang Sun. University College London / CEMFI

Abstract
Researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects. When treatment effects are heterogeneous, however, such specifications generally fail to recover this average effect. Augmenting these specifications with interaction terms between demeaned covariates and treatment eliminates this bias, but often leads to imprecise estimates and becomes infeasible under limited overlap. We propose a generalized ridge regression estimator, regulate, that penalizes the coefficients on the interaction terms to achieve an optimal trade-off between worst-case bias and variance in estimating the average effect under limited treatment effect heterogeneity. Building on this estimator, we construct confidence intervals that remain valid under limited overlap and can also be used to assess sensitivity to violations of the constant effects assumption. We illustrate the method in empirical applications under unconfoundedness and staggered adoption, providing a practical approach to inference under limited overlap.

Biography
Dr. Liyang Sun is an econometrician with research interests in causal inference and weak identification. She primarily focuses on developing new methods for causal inference in a more realistic setting of treatment effects heterogeneity. She has also contributed research on weak identification with many instruments. She is a Lecturer in the Department of Economics at University College London and an Untenured Associate Professor (on leave) at CEMFI.