Individualized Policy Evaluation and Learning under Clustered Network Interference
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Yi, Imai, Kosuke |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs
by: Zhang, Yi, et al.
Published: (2022)
by: Zhang, Yi, et al.
Published: (2022)
Policy Targeting under Network Interference
by: Viviano, Davide
Published: (2019)
by: Viviano, Davide
Published: (2019)
Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024)
by: Zhan, Ruohan, et al.
Published: (2024)
The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021)
by: Auerbach, Eric, et al.
Published: (2021)
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
by: Shirani, Sadegh, et al.
Published: (2025)
by: Shirani, Sadegh, et al.
Published: (2025)
Data-Automated Policy Learning for Nonlinear Welfare
by: Ai, Chunrong, et al.
Published: (2026)
by: Ai, Chunrong, et al.
Published: (2026)
Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
by: Sakaguchi, Shosei
Published: (2021)
by: Sakaguchi, Shosei
Published: (2021)
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)
by: Zhan, Ruohan, et al.
Published: (2021)
Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
by: Imai, Kosuke, et al.
Published: (2025)
by: Imai, Kosuke, et al.
Published: (2025)
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
by: Si, Nian
Published: (2023)
by: Si, Nian
Published: (2023)
Improved Inference for CSDID Using the Cluster Jackknife
by: Karim, Sunny R., et al.
Published: (2026)
by: Karim, Sunny R., et al.
Published: (2026)
Evaluating Policy Effects under Network Interference without Network Information: A Transfer Learning Approach
by: Hoshino, Tadao
Published: (2025)
by: Hoshino, Tadao
Published: (2025)
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)
by: Liu, Nan, et al.
Published: (2024)
Policy design in experiments with unknown interference
by: Viviano, Davide, et al.
Published: (2020)
by: Viviano, Davide, et al.
Published: (2020)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
by: Kato, Masahiro, et al.
Published: (2025)
by: Kato, Masahiro, et al.
Published: (2025)
Experimental Design under Network Interference
by: Viviano, Davide
Published: (2020)
by: Viviano, Davide
Published: (2020)
Causal-Policy Forest for End-to-End Policy Learning
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Policy Learning with Distributional Welfare
by: Cui, Yifan, et al.
Published: (2023)
by: Cui, Yifan, et al.
Published: (2023)
General Bayesian Policy Learning
by: Kato, Masahiro
Published: (2026)
by: Kato, Masahiro
Published: (2026)
Extreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals
by: Sasaki, Yuya, et al.
Published: (2024)
by: Sasaki, Yuya, et al.
Published: (2024)
Cluster-Randomized Trials with Cross-Cluster Interference
by: Leung, Michael P.
Published: (2023)
by: Leung, Michael P.
Published: (2023)
Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
by: Chen, Hongyu, et al.
Published: (2026)
by: Chen, Hongyu, et al.
Published: (2026)
Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
by: Akgun, Oguzhan, et al.
Published: (2025)
by: Akgun, Oguzhan, et al.
Published: (2025)
Adaptive Experimental Design for Policy Learning
by: Kato, Masahiro, et al.
Published: (2024)
by: Kato, Masahiro, et al.
Published: (2024)
Deep Learning With DAGs
by: Balgi, Sourabh, et al.
Published: (2024)
by: Balgi, Sourabh, et al.
Published: (2024)
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)
by: Adedayo, S. A.
Published: (2025)
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
by: Huch, Easton, et al.
Published: (2026)
by: Huch, Easton, et al.
Published: (2026)
STEEL: Singularity-aware Reinforcement Learning
by: Chen, Xiaohong, et al.
Published: (2023)
by: Chen, Xiaohong, et al.
Published: (2023)
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022)
by: Rambachan, Ashesh, et al.
Published: (2022)
An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)
by: Ahrens, Achim, et al.
Published: (2025)
Machine-Learning-Assisted Comparison of Regression Functions
by: Yan, Jian, et al.
Published: (2025)
by: Yan, Jian, et al.
Published: (2025)
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
by: Kallus, Nathan, et al.
Published: (2022)
by: Kallus, Nathan, et al.
Published: (2022)
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
by: Petrungaro, Bruno, et al.
Published: (2026)
by: Petrungaro, Bruno, et al.
Published: (2026)
Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
by: Sakaguchi, Shosei
Published: (2024)
by: Sakaguchi, Shosei
Published: (2024)
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025)
by: Zhou, Guanhao, et al.
Published: (2025)
Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
by: Zeng, Hao, et al.
Published: (2024)
by: Zeng, Hao, et al.
Published: (2024)
Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
by: Baiardi, Anna, et al.
Published: (2026)
by: Baiardi, Anna, et al.
Published: (2026)
xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
by: Polselli, Annalivia
Published: (2025)
by: Polselli, Annalivia
Published: (2025)
Similar Items
-
Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs
by: Zhang, Yi, et al.
Published: (2022) -
Policy Targeting under Network Interference
by: Viviano, Davide
Published: (2019) -
Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024) -
The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021) -
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
by: Shirani, Sadegh, et al.
Published: (2025)