Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data
Lev V. Utkin, Stanislav K. Kogan, Andrei V. Konstantinov
cs.LG, stat.ML
Submitted: 2026-08-06
Code: https://github.com/NTAILab/SurvIPTB
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- High confidence inference on the probability an individual benefits from treatment using experimental or observational data with known propensity scores
- Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach
- False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments
- Enabling Counterfactual Survival Analysis with Balanced Representations
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks