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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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TitleStatusHype
CausalPFN: Amortized Causal Effect Estimation via In-Context LearningCode2
Estimating individual treatment effect: generalization bounds and algorithmsCode1
On Inductive Biases for Heterogeneous Treatment Effect EstimationCode1
Quasi-Oracle Estimation of Heterogeneous Treatment EffectsCode1
Exploring Transformer Backbones for Heterogeneous Treatment Effect EstimationCode1
Data-Driven Estimation of Heterogeneous Treatment Effects—0
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
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—0
Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising—0
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation—0
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