Controlling for group-level heterogeneity in causal forest

29 Pages Posted: 20 Aug 2021

See all articles by Candace Jens

Candace Jens

Tulane University - A.B. Freeman School of Business

T. Beau Page

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

James Reeder, III

Purdue University - Krannert School of Management

Date Written: June 15, 2021

Abstract

Causal forest is part of a growing class of doubly-robust machine learning based estimators that non-parametrically recovers heterogeneity in treatment effects. However, causal forest’s usefulness is currently limited because the group-level heterogeneity present in many economics settings violates a key assumption of causal forest required for the recovery of unbiased effects. We provide a solution: estimate group-level fixed effects in a regression, create a vector of fixed effects coefficients, and include this vector in the casual forest estimation. Monte Carlo simulations show our solution’s success and the shortcomings of alternatives. Our study greatly increases the number of settings in which unbiased, heterogeneous treatment effects are recoverable.

Keywords: causal forest, LASSO, fixed effects, unobserved heterogeneity, panel data.

JEL Classification: C10, C14, C31.

Suggested Citation

Jens, Candace and Page, Beau and Reeder, III, James, Controlling for group-level heterogeneity in causal forest (June 15, 2021). Available at SSRN: https://ssrn.com/abstract=3907601 or http://dx.doi.org/10.2139/ssrn.3907601

Candace Jens (Contact Author)

Tulane University - A.B. Freeman School of Business ( email )

7 McAlister Drive
New Orleans, LA 70118
United States

Beau Page

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

James Reeder, III

Purdue University - Krannert School of Management ( email )

1310 Krannert Building
West Lafayette, IN 47907-1310
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
91
Abstract Views
633
rank
362,069
PlumX Metrics