5 citations · 6 across the 7 of their papers we have counts for
7 papers
StochTree: BART-based modeling in R and Python
Andrew Herren, P. Richard Hahn, Jared Murray +1
stochtree is a C++ library for Bayesian tree ensemble models such as BART and Bayesian Causal Forests (BCF), as well as user-specified variations. Unlike previous BART packages, st…
Minnesota BART
Pedro A. Lima, Carlos M. Carvalho, Hedibert F. Lopes +1
Vector autoregression (VAR) models are widely used for forecasting and macroeconomic analysis, yet they remain limited by their reliance on a linear parameterization. Recent resear…
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
Demetrios Papakostas, Andrew Herren, P. Richard Hahn +1
Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between. Concurrently, the study and us…
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…
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…