:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Jennifer Y., Du, Shuyang, Zou, Will Y.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	https://arxiv.org/abs/2507.05511
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
by: Du, Shuyang, et al.
Published: (2025)

Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
by: Schuessler, Maximilian, et al.
Published: (2025)

Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data
by: Zhang, Yi, et al.
Published: (2024)

A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
by: Du, Honglin, et al.
Published: (2026)

Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
by: Liang, Zhongyuan, et al.
Published: (2025)

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
by: Kacprzyk, Krzysztof, et al.
Published: (2024)

A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
by: Fisher, Aaron
Published: (2023)

Continuous Treatment Effects with Surrogate Outcomes
by: Zeng, Zhenghao, et al.
Published: (2024)

Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects
by: Yao, Dong, et al.
Published: (2024)

A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes
by: Salmaso, Filippo, et al.
Published: (2026)

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
by: Persson, Joel, et al.
Published: (2026)

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
by: Oprescu, Miruna, et al.
Published: (2024)

What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
by: Dandl, Susanne, et al.
Published: (2022)

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022)

Data-Driven Estimation of Heterogeneous Treatment Effects
by: Tran, Christopher, et al.
Published: (2023)

Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
by: Shyr, Cathy, et al.
Published: (2023)

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
by: Chikahara, Yoichi, et al.
Published: (2024)

Doubly Robust Fusion of Many Treatments for Policy Learning
by: Zhu, Ke, et al.
Published: (2025)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
by: Chen, Shuyuan, et al.
Published: (2026)

Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
by: Xu, Shenbo, et al.
Published: (2024)

M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
by: Li, Xingyu, et al.
Published: (2025)

Doubly Robust Proximal Causal Learning for Continuous Treatments
by: Wu, Yong, et al.
Published: (2023)

Accounting for Missing Covariates in Heterogeneous Treatment Estimation
by: Yamin, Khurram, et al.
Published: (2024)

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
by: Zhao, Boxin, et al.
Published: (2021)

Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, et al.
Published: (2024)

Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond
by: Morzywolek, Pawel, et al.
Published: (2025)

Learning Individual Treatment Effects under Heterogeneous Interference in Networks
by: Zhao, Ziyu, et al.
Published: (2022)

Aggregating Conformal Prediction Sets via α-Allocation
by: Xu, Congbin, et al.
Published: (2025)

Distributional Treatment Effect Estimation across Heterogeneous Sites via Optimal Transport
by: Bateni, Borna, et al.
Published: (2025)

A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
by: Pan, Jing, et al.
Published: (2025)

Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
by: Nanavati, Praharsh, et al.
Published: (2025)

CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
by: Zhou, Jiehui, et al.
Published: (2024)

Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes
by: Lu, Yuying, et al.
Published: (2026)

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
by: Zhang, Ruijia, et al.
Published: (2025)

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by: Jin, Jikai, et al.
Published: (2024)

DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
by: Ghosh, Shantanu, et al.
Published: (2023)

Treatment Effects in Extreme Regimes
by: Aloui, Ahmed, et al.
Published: (2023)