The heterogeneous effects of default on investment: An application of causal forest in corporate finance

99 Pages Posted: 20 May 2020 Last revised: 27 Dec 2021

See all articles by Huseyin Gulen

Huseyin Gulen

Purdue University - Krannert School of Management

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)

Date Written: December 26, 2021

Abstract

Answering causal questions with extendable results is challenging. Regression discontinuity design (RDD) recovers selection-bias-free estimates that are uninformative outside of the threshold sample. Using Monte Carlo experiments, we compare the performance of RDD against causal forest, a non-parametric, machine-learning-based matching estimator, at recovering estimates in panel data. Even in simulations with selection bias, causal forest recovers estimates that are low-bias and much more precise than RDD estimates. Consequently, causal forest commonly outperforms RDD at recovering “true” treatment effects. We re-visit a popular RDD design, debt covenant defaults, to show in practice how extendable and heterogeneous causal forest estimates 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, The heterogeneous effects of default on investment: An application of causal forest in corporate finance (December 26, 2021). 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)

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

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