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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
Drew Herren’s Personal Website
Posts
Experimenting with tree models using the scikit-learn codebase
Published:
TL;DR: scikit-tree extends the scikit-learn tree codebase to enable fast, cython-accelerated experimentation with tree algorithms.
portfolio
scikit-tree
Cython-based library for rapid experimentation with tree methods
Implementations
Minimal didactic implementations of common algorithms in statistics and applied math
Semi-supervised Propensity Score Methods
Simulation studies on the use of unlabeled data in causal inference
The Relationship between SHAP and Functional ANOVA
Implementations and simulation studies that detail the links between SHAP and the functional ANOVA, including possible modifications to SHAP
publications
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
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
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
talks
Feature selection for learning causal effects: a finite-sample, stratification estimator perspective
Published:
I presented my ongoing work on feature selection in causal inference at the 2022 NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics (SBIES). Slides from my talk are available here