Balancing external vs. internal validity: An application of causal forest in finance
92 Pages Posted: 20 May 2020 Last revised: 23 Aug 2022
Date Written: May 12, 2022
Answering a causal question with results extendable outside of a narrow sample is challenging. Regression discontinuity design (RDD) provides results with strong internal but weak external validity. 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. We show causal forest’s observation-level, heterogeneous treatment effects are robust to confounding so bias is low in many settings. Moreover, any potential bias in forest estimates can be bounded. We re-visit a popular RDD design, debt covenant defaults, to show 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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