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Found 4 papers, 0 papers with code
Regularized DeepIV with Model Selection
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Causal Q-Aggregation for CATE Model Selection
We provide regret rates for the major existing CATE ensembling approaches and propose a new CATE model ensembling approach based on Q-aggregation using the doubly robust loss.
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
