Abstract:
A wide range of problems in causal inference can be framed as estimating the conditional expectation of a moment functional that depends on high-dimensional nuisance functions. In this paper, we propose a new method, the automatic Doubly Robust Random Forest (DRRF), to address this challenge. DRRF extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation. In contrast to existing methods, DRRF does not require prior knowledge of the form of the debiasing term or impose restrictive parametric or semi-parametric assumptions on the target quantity. Additionally, it is computationally efficient in making predictions at multiple query points. We establish consistency and asymptotic normality results for the DRRF estimator under general assumptions, allowing for the construction of valid confidence intervals. Through extensive simulations in heterogeneous treatment effect (HTE) estimation, we demonstrate the superior performance of DRRF over benchmark approaches in terms of estimation accuracy, robustness, and computational efficiency.
About the Speaker:
Junting Duan is a fifth-year Ph.D. student in the Department of Management Science and Engineering at Stanford University. Her research interests lie broadly in data-driven decision-making, machine learning, and statistical inference, with a particular focus on applications in causal inference and finance.
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