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Found 5 papers, 4 papers with code
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making.
Propensity score models are better when post-calibrated
We find that post-calibration reduces the error in effect estimation for expressive uncalibrated statistical estimators, and that this improvement is not mediated by better balancing.
A discriminative approach for finding and characterizing positivity violations using decision trees
We provide a visualization of the stratification rules that define each subpopulation, combined with the severity of positivity violation within it.
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
Thus, the toolkit is agnostic to the machine learning model that is used.
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Causal inference analysis is the estimation of the effects of actions on outcomes.
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