Research

Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

arXiv, 2022

Insights on Feature Selection for Treatment Effect Estimation.

Recommended citation: Hahn, P. R. & Herren, A. (2022). Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations. arXiv preprint arXiv:2209.11400. https://arxiv.org/abs/2209.11400

Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation

arXiv, 2022

Connections between SHAP and the design of experiments and sensitivity analysis literature.

Recommended citation: Herren, A., & Hahn, P. R. (2022). Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation. arXiv preprint arXiv:2208.09970. https://arxiv.org/abs/2208.09970

Semi-supervised learning and the question of true versus estimated propensity scores

arXiv, 2020

Whether (and when) to use unlabeled data in causal inference via the propensity score.

Recommended citation: Herren, A., & Hahn, P. R. (2020). Semi-supervised learning and the question of true versus estimated propensity scores. arXiv preprint arXiv:2009.06183. https://arxiv.org/abs/2009.06183

Drew Herren

Research

Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation

Semi-supervised learning and the question of true versus estimated propensity scores