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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
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes—0
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace—0
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game—0
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—0
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder—0
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests—0
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition—0
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers—0
The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest—0
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