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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 1–10 of 38 papers

TitleStatusHype
CausalPFN: Amortized Causal Effect Estimation via In-Context LearningCode2
TabPFN: One Model to Rule Them All?Code0
Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising—0
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
Class flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data—0
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition—0
Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution PopulationsCode0
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation—0
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect EstimationCode0
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder—0
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