Balancing External vs. Internal Validity: An Application of Causal Forest in Finance
比较了普通最小二乘法、断点回归设计和因果森林在模拟面板数据中的表现，发现因果森林在存在内生性时仍能提供低偏误、可推广的估计，并展示了其在债务契约违约研究中的应用。
Answering causal questions with generalizable results is challenging. Estimators requiring pseudorandomization provide estimates with no bias (i.e., strong internal validity) but limited generalizability (i.e., weak external validity). Theoretically, causal forest, a nonparametric, machine learning–based matching estimator, can provide low-to-no-bias, generalizable estimates even when treatment is endogenous. We empirically compare the performance of ordinary least squares (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 revisit a popular RDD setting, debt covenant default, to show how extendable, heterogeneous causal forest estimates can enhance inferences. This paper was accepted by Tomasz Piskorski, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00109 .