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

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