Balancing external vs. internal validity: An application of causal forest in finance

99 Pages Posted: 20 May 2020 Last revised: 2 Nov 2022

See all articles by Huseyin Gulen

Huseyin Gulen

Purdue University - Krannert School of Management

Candace Jens

Syracuse University - Whitman School of Management

T. Beau Page

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

Date Written: October 24, 2022

Abstract

Answering causal questions with generalizable results is challenging. Estimators requiring pseudo-randomization provide estimates with no bias (i.e., strong internal validity) but limited generalizability (i.e., weak external validity). Theoretically, causal forest, a non-parametric, machine-learning-based matching estimator, can provide low-to-no-bias, generalizable estimates even when treatment is endogenous. We empirically compare the performance of OLS, regression discontinuity design (RDD), and causal forest at recovering estimates in simulated observational panel data and show the robustness of causal forest estimates to many sources of bias. We re-visit a popular RDD setting, debt covenant default, to show how extendable, heterogeneous causal forest estimates can enhance inferences.

Keywords: causal forest, investment, financing, RDD, machine learning

JEL Classification: G32, G31, C50

Suggested Citation

Gulen, Huseyin and Jens, Candace and Page, Beau, Balancing external vs. internal validity: An application of causal forest in finance (October 24, 2022). Available at SSRN: https://ssrn.com/abstract=3583685 or http://dx.doi.org/10.2139/ssrn.3583685

Huseyin Gulen

Purdue University - Krannert School of Management ( email )

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

Candace Jens (Contact Author)

Syracuse University - Whitman School of Management ( email )

721 University Avenue
Syracuse, NY 13244-2130
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

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