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Heterogeneous Treatment Effect Estimation

Heterogeneous treatment effect (HTE) estimation is the task of quantifying how treatment effects vary across different individuals or subgroups.

Papers

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Showing 26–38 of 38 papers

TitleStatusHype
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event DataCode0
Learning Triggers for Heterogeneous Treatment EffectsCode0
Local Linear ForestsCode0
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear ModelCode0
Non-Parametric Inference Adaptive to Intrinsic DimensionCode0
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect EstimationCode0
Generalized Random ForestsCode0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Some methods for heterogeneous treatment effect estimation in high-dimensionsCode0
Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution PopulationsCode0
TabPFN: One Model to Rule Them All?Code0
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure UncertaintyCode0
Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared NeuronsCode0
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