223 citations · 335 across the 19 of their papers we have counts for
4 papers · 1 filter
Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
P. Richard Hahn, Andrew Herren
What is the ideal regression (if any) for estimating average causal effects? We study this question in the setting of discrete covariates, deriving expressions for the finite-sampl…
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for caus…
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Andrew Herren, P. Richard Hahn, Rafael Alcantara
SHAP is a popular method for measuring variable importance in machine learning models. In this paper, we study the algorithm used to estimate SHAP scores and outline its connection…
Local Gaussian process extrapolation for BART models with applications to causal inference
Meijiang Wang, Jingyu He, P. Richard Hahn
Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard im…