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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 11–20 of 38 papers

TitleStatusHype
Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared NeuronsCode0
Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning—0
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear ModelCode0
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making—0
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers—0
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event DataCode0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Data-Driven Estimation of Heterogeneous Treatment Effects—0
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests—0
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure UncertaintyCode0
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