Fixed Effects and the Generalized Mundlak Estimator

Abstract

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 07, 2023
Source ID
10.1093/restud/rdad089

Entities

People

  • Dmitry Arkhangelsky
  • Guido Imbens

Organizations

  • CEMFI
  • Office of Naval Research
  • Stanford University

Tags

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Organizational Psychology.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms