3 papers
stat.ML2024
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…
stat.ME2022
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…
stat.ME2020
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…