Picture for Andrew Herren

Andrew Herren

Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

Add code
May 06, 2024
Viaarxiv icon

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

Add code
Sep 23, 2022
Figure 1 for Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Figure 2 for Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Figure 3 for Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Figure 4 for Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Viaarxiv icon

Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation

Add code
Aug 21, 2022
Figure 1 for Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Figure 2 for Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Figure 3 for Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Figure 4 for Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Viaarxiv icon

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

Add code
Sep 14, 2020
Figure 1 for Semi-supervised learning and the question of true versus estimated propensity scores
Figure 2 for Semi-supervised learning and the question of true versus estimated propensity scores
Figure 3 for Semi-supervised learning and the question of true versus estimated propensity scores
Figure 4 for Semi-supervised learning and the question of true versus estimated propensity scores
Viaarxiv icon