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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
Exploring Transformer Backbones for Heterogeneous Treatment Effect EstimationCode1
On Inductive Biases for Heterogeneous Treatment Effect EstimationCode1
Quasi-Oracle Estimation of Heterogeneous Treatment EffectsCode1
Estimating individual treatment effect: generalization bounds and algorithmsCode1
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
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