[CSSL@CUHK Webinar] Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization


19 Apr 2024


9:00 am to 10:30 am (UTC+8,HKT)




Prof. Phillip Heiler

Biography of Speaker:

Phillip Heiler is an Associate Professor at Aarhus University at the Department of Economics and Business Economics and the TrygFonden’s Centre for Child Research (on leave) and currently visiting Associate Professor at the Department of Economics at Harvard University. His main research interests are econometrics of causal inference, causal machine learning, non- and semiparametric econometrics, and partial identification.

Synopsis of Lecture:

We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters in general sample selection models where the treatment can affect whether an outcome is observed and no exclusion restrictions are available. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effects. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders. Easily verifiable high-level conditions for estimation, misspecification robust confidence intervals, and uniform confidence bands are provided as well. We re-analyze data from a large scale field experiment on Facebook on counter-attitudinal news subscription with attrition. Our method yields substantially tighter effect bounds compared to conventional methods and suggests depolarization effects for younger users.