Cover Image

Accounting for Missing Covariates in Heterogeneous Treatment Estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yamin, Khurram, Sharma, Vibhhu, Kennedy, Ed, Wilder, Bryan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources
by: Sharma, Vibhhu, et al.
Published: (2024)

Dependent Randomized Rounding for Budget Constrained Experimental Design
by: Yamin, Khurram, et al.
Published: (2025)

Learning treatment effects while treating those in need
by: Wilder, Bryan, et al.
Published: (2024)

Can LLMs Reconcile Knowledge Conflicts in Counterfactual Reasoning
by: Yamin, Khurram, et al.
Published: (2025)

Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization
by: Lee, JungHo, et al.
Published: (2026)

Can Revealed Preferences Clarify LLM Alignment and Steering?
by: Yamin, Khurram, et al.
Published: (2026)

DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation
by: Khan, Ahmad Saeed, et al.
Published: (2025)

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

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
by: Kato, Masahiro, et al.
Published: (2024)

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

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)

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

The Cellwise Minimum Covariance Determinant Estimator
by: Raymaekers, Jakob, et al.
Published: (2022)

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

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

Efficient Covariance Estimation for Sparsified Functional Data
by: Zheng, Sijie, et al.
Published: (2025)

Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
by: Xu, Shenbo, 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)

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

Weighting-Based Identification and Estimation in Graphical Models of Missing Data
by: Guo, Anna, et al.
Published: (2026)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
by: Chihara, Naoki, et al.
Published: (2026)

Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
by: Hou, Jue, et al.
Published: (2019)

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

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

A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
by: Pham, Khiem, et al.
Published: (2023)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
by: Kato, Masahiro
Published: (2025)

Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
by: Wiederkehr, Christoph, et al.
Published: (2025)

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
by: Lin, Jiahe, et al.
Published: (2025)

Spatial Covariance Constraints for Gaussian Mixture Models
by: Lu, Hanzhang, et al.
Published: (2026)

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

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)

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

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

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

Orthogonal Causal Calibration
by: Whitehouse, Justin, et al.
Published: (2024)

Auditing Fairness under Unobserved Confounding
by: Byun, Yewon, et al.
Published: (2024)

Time Series Treatment Effects Analysis with Always-Missing Controls
by: Shu, Juan, et al.
Published: (2025)