YSPH Biostatistics Seminar: “A Generalized Difference-in-Differences Estimator for Causal Inference with Staggered Treatment Adoption"

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Lee Kennedy-Shaffer, PhD, Assistant Professor of Biostatistics

TITLE: “A Generalized Difference-in-Differences Estimator for Causal Inference with Staggered Treatment Adoption"

ABSTRACT: Staggered treatment adoption arises in the evaluation of policy impact and implementation in a variety of settings. This occurs in both randomized stepped-wedge trials and non-randomized quasi-experimental panel data settings using causal inference methods based on difference-in-differences analysis. These have been used to evaluate health and economic policies; group-based interventions in education, health care, and other settings; and the impact of sudden events. In both types of setting, it is crucial to carefully consider the target estimand and possible treatment effect heterogeneities to estimate the effect without bias and in an interpretable fashion. I propose a non-parametric approach to this estimation that unites the two settings. By constructing an estimator using two-by-two difference-in-difference comparisons as building blocks with arbitrary weights, the investigator can select weights to target the desired estimand in an unbiased manner under assumed treatment effect homogeneity and minimize the variance under an assumed working covariance structure. This provides desirable bias properties while using the comparisons efficiently to mitigate the loss of precision. I will first describe these two settings and their role in causal inference and policy evaluation. I then present simple settings to illustrate the underlying ideas of this new method. Finally, I will show an example using the method to identify various treatment effects in a previously-conducted stepped wedge randomized controlled trial on tuberculosis detection methods.

YSPH values inclusion and access for all participants. If you have questions about accessibility or would like to request an accommodation, please contact Charmila Fernandes at Charmila.fernandes@yale.edu. We will try to provide accommodations requested by October 10, 2024.