223 citations · 335 across the 19 of their papers we have counts for
12 papers · 1 filter
On true versus estimated propensity scores for treatment effect estimation with discrete controls
Andrew Herren, P. Richard Hahn
The finite sample variance of an inverse propensity weighted estimator is derived in the case of discrete control variables with finite support. The obtained expressions generally…
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…
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…
Semi-supervised learning and the question of true versus estimated propensity scores
Andrew Herren, P. Richard Hahn
A straightforward application of semi-supervised machine learning to the problem of treatment effect estimation would be to consider data as "unlabeled" if treatment assignment and…
A Symmetric Prior for Multinomial Probit Models
Lane F. Burgette, David Puelz, P. Richard Hahn
Fitted probabilities from widely used Bayesian multinomial probit models can depend strongly on the choice of a base category, which is used to uniquely identify the parameters of…