Cover Image

Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fuchs, Michael, Kreiss, Dominik
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
by: Jain, Saksham, et al.
Published: (2026)

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

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

Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
by: Sert, Gözde, et al.
Published: (2025)

Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
by: McClean, Alec, 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)

Doubly Robust Conformalized Survival Analysis with Right-Censored Data
by: Sesia, Matteo, et al.
Published: (2024)

Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)

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

Forests for Differences: Robust Causal Inference Beyond Parametric DiD
by: Souto, Hugo Gobato, et al.
Published: (2025)

Doubly Robust Inference in Causal Latent Factor Models
by: Abadie, Alberto, et al.
Published: (2024)

A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
by: Singh, Aman
Published: (2026)

Automatic Doubly Robust Forests
by: Chen, Zhaomeng, et al.
Published: (2024)

Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization
by: Jin, Ying, et al.
Published: (2024)

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

Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
by: Witter, R. Teal, et al.
Published: (2024)

Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
by: Lee, Byeonghee, et al.
Published: (2025)

Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners
by: Knowlton, Rebecca, et al.
Published: (2025)

Direct Doubly Robust Estimation of Conditional Quantile Contrasts
by: Givens, Josh, et al.
Published: (2026)

A Tutorial on Doubly Robust Learning for Causal Inference
by: Hlynsson, Hlynur Davíð
Published: (2024)

Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
by: Khatami, Seyedeh Baharan, et al.
Published: (2024)

Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts
by: Kern, Christoph, et al.
Published: (2024)

Doubly-Regressing Approach for Subgroup Fairness
by: Kim, Kunwoong, et al.
Published: (2025)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
by: Kallus, Nathan, et al.
Published: (2022)

Double Robust Bayesian Inference on Average Treatment Effects
by: Breunig, Christoph, et al.
Published: (2022)

Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
by: Shen, Ye, et al.
Published: (2021)

Off-policy Evaluation in Doubly Inhomogeneous Environments
by: Bian, Zeyu, et al.
Published: (2023)

Golden Ratio-Based Sufficient Dimension Reduction
by: Yang, Wenjing, et al.
Published: (2024)

A Note on Doubly Robust Estimator in Regression Discontinuity Designs
by: Kato, Masahiro
Published: (2024)

Doubly robust identification of treatment effects from multiple environments
by: De Bartolomeis, Piersilvio, et al.
Published: (2025)

Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional
by: McGrath, Sean, et al.
Published: (2022)

Doubly Robust Estimation of Treatment Effects in Staggered Difference-in-Differences with Time-Varying Covariates
by: Deng, Yuhao, et al.
Published: (2026)

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

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

Proximal Projection for Doubly Sparse Regularized Models
by: He, Jia Wei, et al.
Published: (2026)

Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
by: McGrath, Sean, et al.
Published: (2025)

The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
by: Zhang, Yuqian, et al.
Published: (2023)

Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
by: Kato, Masahiro, et al.
Published: (2023)