Similar Items
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)
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)
Individualized Policy Evaluation and Learning under Clustered Network Interference
by: Zhang, Yi, et al.
Published: (2023)
by: Zhang, Yi, et al.
Published: (2023)
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)
The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021)
by: Auerbach, Eric, et al.
Published: (2021)
LGB+: A Macroeconomic Forecasting Road Test
by: Coulombe, Philippe Goulet
Published: (2026)
by: Coulombe, Philippe Goulet
Published: (2026)
Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
by: Chen, Shuze, et al.
Published: (2024)
by: Chen, Shuze, et al.
Published: (2024)
A Causal Inference Framework for Data Rich Environments
by: Abadie, Alberto, et al.
Published: (2025)
by: Abadie, Alberto, et al.
Published: (2025)
Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models
by: Breunig, Christoph, et al.
Published: (2020)
by: Breunig, Christoph, et al.
Published: (2020)
A Double Machine Learning Approach to Combining Experimental and Observational Data
by: Parikh, Harsh, et al.
Published: (2023)
by: Parikh, Harsh, et al.
Published: (2023)
Data Fusion for Partial Identification of Causal Effects
by: Lanners, Quinn, et al.
Published: (2025)
by: Lanners, Quinn, 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)
Adaptive Principal Component Regression with Applications to Panel Data
by: Agarwal, Anish, et al.
Published: (2023)
by: Agarwal, Anish, et al.
Published: (2023)
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)
by: Liu, Nan, et al.
Published: (2024)
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
by: Yan, Yuling, et al.
Published: (2024)
by: Yan, Yuling, et al.
Published: (2024)
Causal Mediation Analysis with Multiple Mediators: A Simulation Approach
by: Zhou, Jesse, et al.
Published: (2025)
by: Zhou, Jesse, et al.
Published: (2025)
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)
Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)
by: Imbens, Guido, et al.
Published: (2022)
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)
Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
by: Yang, Xuelin, et al.
Published: (2025)
by: Yang, Xuelin, et al.
Published: (2025)
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)
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)
Unconditional Randomization Tests for Interference
by: Zhong, Liang
Published: (2024)
by: Zhong, Liang
Published: (2024)
Policy Targeting under Network Interference
by: Viviano, Davide
Published: (2019)
by: Viviano, Davide
Published: (2019)
A Gentle Introduction to Conformal Time Series Forecasting
by: Stocker, M., et al.
Published: (2025)
by: Stocker, M., et al.
Published: (2025)
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
by: Agarwal, Abhineet, et al.
Published: (2023)
by: Agarwal, Abhineet, et al.
Published: (2023)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
by: Parikh, Harsh, et al.
Published: (2025)
by: Parikh, Harsh, et al.
Published: (2025)
A step towards the integration of machine learning and classic model-based survey methods
by: Żądło, Tomasz, et al.
Published: (2024)
by: Żądło, Tomasz, et al.
Published: (2024)
Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
by: Gao, Zhaoxing, et al.
Published: (2024)
by: Gao, Zhaoxing, et al.
Published: (2024)
Testing Most Influential Sets
by: Konrad, Lucas Darius, et al.
Published: (2025)
by: Konrad, Lucas Darius, et al.
Published: (2025)
Experimental Designs for Multi-Item Multi-Period Inventory Control
by: Chen, Xinqi, et al.
Published: (2025)
by: Chen, Xinqi, et al.
Published: (2025)
Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
by: Langevin, Raphaël
Published: (2026)
by: Langevin, Raphaël
Published: (2026)
Statistical Tests for Replacing Human Decision Makers with Algorithms
by: Feng, Kai, et al.
Published: (2023)
by: Feng, Kai, et al.
Published: (2023)
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
by: Athey, Susan, et al.
Published: (2023)
by: Athey, Susan, et al.
Published: (2023)
Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
by: Duda, Jarek
Published: (2023)
by: Duda, Jarek
Published: (2023)
Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
by: Kato, Masahiro, et al.
Published: (2023)
by: Kato, Masahiro, et al.
Published: (2023)
STEEL: Singularity-aware Reinforcement Learning
by: Chen, Xiaohong, et al.
Published: (2023)
by: Chen, Xiaohong, et al.
Published: (2023)
The Bayesian Context Trees State Space Model for time series modelling and forecasting
by: Papageorgiou, Ioannis, et al.
Published: (2023)
by: Papageorgiou, Ioannis, et al.
Published: (2023)
Similar Items
-
Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024) -
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021) -
Individualized Policy Evaluation and Learning under Clustered Network Interference
by: Zhang, Yi, et al.
Published: (2023) -
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
by: Shirani, Sadegh, et al.
Published: (2025) -
The Local Approach to Causal Inference under Network Interference
by: Auerbach, Eric, et al.
Published: (2021)