Tom Zohar, CEMFI

On Causal Inference with Model-Based Outcomes

Abstract

We study a causal inference problem with group-level outcomes, which are themselves parameters identified from microdata. We formalize these outcomes using population moment conditions and demonstrate that one-step Generalized Method of Moments (GMM) estimators are generally inconsistent due to an endogenous weighting bias, where policy affects the implicit GMM weights. In contrast, two-stage Minimum Distance (MD) estimators perform well when group sizes are sufficiently large. While MD estimators can still be inconsistent in small groups due to a policy-induced sample selection, we demonstrate that this can be addressed by incorporating auxiliary population information. An empirical application illustrates the practical importance of these findings.

Tom Zohar is an Assistant Professor in Economics at CEMFI, and a Research Affiliate at CESifo.

He works on labor market inequality and it's relationship with workers' mobility. He is also interested in the role of social norms on fertility decisions and it's implications to labor supply.

Tom Zohar received his PhD in Economics from Stanford University in 2021.

You can read more about Tom Zohar and his research here

CEBI contact: N. Meltem Daysal.