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
Date Written: December 26, 2021
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
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