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Found 3 papers, 0 papers with code
Estimating heterogeneous treatment effect from survival outcomes via (orthogonal) censoring unbiased learning
In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks. After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous outcomes yields consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects.
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.
Tensor Dropout for Robust Learning
CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets.
