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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 1–25 of 38 papers

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
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
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
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability—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
Class flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data—0
Data-Driven Estimation of Heterogeneous Treatment Effects—0
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation—0
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game—0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—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
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
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