MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
and Polley, Eric C
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
verdicts
UNVERDICTED 8representative citing papers
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.
A data-supported estimand for the effect of flexible shifts in multi-component exposure mixtures is defined and estimated nonparametrically with machine learning.
Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.
citing papers explorer
-
Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy
MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
-
Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
-
A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
-
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.
-
A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
-
Soft Learning
Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.
-
Everything all at once: On choosing an estimand for multi-component environmental exposures
A data-supported estimand for the effect of flexible shifts in multi-component exposure mixtures is defined and estimated nonparametrically with machine learning.
-
Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.