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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
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes—0
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles—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
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods—0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—0
The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest—0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Learning Triggers for Heterogeneous Treatment EffectsCode0
Non-Parametric Inference Adaptive to Intrinsic DimensionCode0
Local Linear ForestsCode0
Some methods for heterogeneous treatment effect estimation in high-dimensionsCode0
Generalized Random ForestsCode0
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