Robust Causal Estimation using Random Forests

Music City Center 

An increased number of outliers presents a major challenge in data analysis, yielding uninterpretable and often biased results when using mean-based statistical models. In causal inference, the classic average causal effect is defined as the population mean difference between counterfactual outcomes under treatment and control. Thus, causal inference for Mann-Whitney-Wilcoxon rank sum test (MWWRST) has been proposed to address these outlier-related challenges and provide a more robust inference. However, these methods assume logistic regression for outcome modeling, which impose restrictions when the number of covariates increases or when complex interactions are present. In this talk, I will introduce a novel approach to estimating the causal effects for MWWRST using random forests. By extending the causal forest framework of Wager and Athey (2018), we demonstrate that our estimator is pointwise consistent and asymptotically normal, providing a flexible and robust alternative for causal inference in nonparametric settings.

Mann-Whitney-Wilcoxon rank sum test

U-statistics

Functional response models