Cover Image

Treatment Effect Estimation with Observational Network Data using Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Emmenegger, Corinne, Spohn, Meta-Lina, Elmer, Timon, Bühlmann, Peter
Format:	Preprint
Published:	2022
Subjects:	Methodology Statistics Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
by: Guo, Zijian, et al.
Published: (2022)

Extrapolation-Aware Nonparametric Statistical Inference
by: Pfister, Niklas, et al.
Published: (2024)

Estimation of Treatment Effects in Extreme and Unobserved Data
by: Tan, Jiyuan, et al.
Published: (2025)

Causal Invariance Learning via Efficient Nonconvex Optimization
by: Wang, Zhenyu, et al.
Published: (2024)

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
by: Sakaguchi, Shosei
Published: (2024)

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
by: Ghassami, AmirEmad, et al.
Published: (2022)

Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
by: Kovács, Solt, et al.
Published: (2020)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
by: Gu, Yihong, et al.
Published: (2024)

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

Design Stability in Adaptive Experiments: Implications for Treatment Effect Estimation
by: Sengupta, Saikat, et al.
Published: (2025)

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

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

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

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
by: Kato, Masahiro
Published: (2025)

Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)

A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
by: Chakrabortty, Abhishek, et al.
Published: (2022)

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

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
by: Kato, Masahiro
Published: (2025)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
by: Cai, Yang, et al.
Published: (2025)

Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
by: Wang, Xiaohan, et al.
Published: (2025)

Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time
by: Cwiling, Ariane, et al.
Published: (2024)

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

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

Learning Robust Treatment Rules for Censored Data
by: Cui, Yifan, et al.
Published: (2024)

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

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
by: Cattaneo, Matias D., et al.
Published: (2025)

PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
by: Kato, Masahiro, et al.
Published: (2025)

Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data
by: Zhang, Tianyu, et al.
Published: (2023)

Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
by: Agarwal, Medha, et al.
Published: (2026)

SGD with Dependent Data: Optimal Estimation, Regret, and Inference
by: Shen, Yinan, et al.
Published: (2026)

ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
by: Kato, Masahiro
Published: (2025)

Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials
by: Liu, Yi, et al.
Published: (2025)

Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Data
by: Chen, Ling, et al.
Published: (2024)

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data
by: Jiang, Cong, et al.
Published: (2023)

Perturbation-based Effect Measures for Compositional Data
by: Lundborg, Anton Rask, et al.
Published: (2023)

Active Subsampling for Measurement-Constrained M-Estimation of Individualized Thresholds with High-Dimensional Data
by: Duan, Jingyi, et al.
Published: (2024)

On Nonasymptotic Confidence Intervals for Treatment Effects in Randomized Experiments
by: Sandoval, Ricardo J., et al.
Published: (2026)

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
by: van der Laan, Lars, et al.
Published: (2025)

Identifiability of Sparse Causal Effects using Instrumental Variables
by: Pfister, Niklas, et al.
Published: (2022)

Cross-Balancing for Data-Informed Design and Efficient Analysis of Observational Studies
by: Jin, Ying, et al.
Published: (2025)