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Found 4 papers, 2 papers with code
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between.
Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
What is the ideal regression (if any) for estimating average causal effects?
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
We use this connection to show that challenges in SHAP approximations largely relate to the choice of a feature distribution and the number of $2^p$ ANOVA terms estimated.
Semi-supervised learning and the question of true versus estimated propensity scores
According to this formulation, large unlabeled data sets could be used to estimate a high dimensional propensity function and causal inference using a much smaller labeled data set could proceed via weighted estimators using the learned propensity scores.
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