Saved in:
| Main Authors: | Wang, Steven, Johnson, Isys, Grogan, Jessica, Jain, Lalit, Rudra, Atri, Hunt, Kyle, Joseph, Kenneth |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.02123 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Adaptive Experimentation When You Can't Experiment
by: Zhao, Yao, et al.
Published: (2024)
by: Zhao, Yao, et al.
Published: (2024)
An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects
by: Chiu, Albert
Published: (2025)
by: Chiu, Albert
Published: (2025)
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
by: Fiez, Tanner, et al.
Published: (2024)
by: Fiez, Tanner, et al.
Published: (2024)
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
by: Xu, Shuoxun, et al.
Published: (2026)
by: Xu, Shuoxun, et al.
Published: (2026)
Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
by: Goplerud, Max, et al.
Published: (2022)
by: Goplerud, Max, et al.
Published: (2022)
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
by: Lee, Seungyeon, et al.
Published: (2024)
by: Lee, Seungyeon, et al.
Published: (2024)
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)
by: Ham, Dae Woong, et al.
Published: (2022)
Learning Preferences from Conjoint Data: A Structural Deep Learning Approach
by: Acharya, Avidit, et al.
Published: (2026)
by: Acharya, Avidit, et al.
Published: (2026)
Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects
by: Sugasawa, Shonosuke, et al.
Published: (2023)
by: Sugasawa, Shonosuke, et al.
Published: (2023)
Subgroup Identification with Latent Factor Structure
by: He, Yong, et al.
Published: (2024)
by: He, Yong, et al.
Published: (2024)
When Sensitivity Bias Varies Across Subgroups: The Impact of Non-uniform Polarity in List Experiments
by: Hatz, Sophia, et al.
Published: (2024)
by: Hatz, Sophia, et al.
Published: (2024)
Fixed-Effects Models for Causal Inference in Longitudinal Cluster Randomized and Quasi-Experimental Trials
by: Lee, Kenneth M., et al.
Published: (2026)
by: Lee, Kenneth M., et al.
Published: (2026)
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
by: Zhou, Jiehui, et al.
Published: (2024)
by: Zhou, Jiehui, et al.
Published: (2024)
Penalized Subgrouping of Heterogeneous Time Series
by: Crawford, Christopher M., et al.
Published: (2024)
by: Crawford, Christopher M., et al.
Published: (2024)
Estimating Interpretable Heterogeneous Treatment Effect with Causal Subgroup Discovery in Survival Outcomes
by: Bo, Na, et al.
Published: (2024)
by: Bo, Na, et al.
Published: (2024)
Subgroup analysis in multi level hierarchical cluster randomized trials
by: Chakraborty, Shubhadeep, et al.
Published: (2024)
by: Chakraborty, Shubhadeep, et al.
Published: (2024)
Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data
by: Schwartz, Daniel, et al.
Published: (2023)
by: Schwartz, Daniel, et al.
Published: (2023)
Minimally Discrete and Minimally Randomized p-Values
by: Habiger, Joshua, et al.
Published: (2026)
by: Habiger, Joshua, et al.
Published: (2026)
Propensity Score Analysis with Guaranteed Subgroup Balance
by: Li, Yan, et al.
Published: (2024)
by: Li, Yan, et al.
Published: (2024)
Decision Theoretic Subgroup Detection With Bayesian Machine Learning
by: Alam, Entejar, et al.
Published: (2025)
by: Alam, Entejar, et al.
Published: (2025)
The Promises of Multiple Experiments: Identifying Joint Distribution of Potential Outcomes
by: Wu, Peng, et al.
Published: (2025)
by: Wu, Peng, et al.
Published: (2025)
The Bias-Variance Tradeoff in Long-Term Experimentation
by: Ting, Daniel, et al.
Published: (2025)
by: Ting, Daniel, et al.
Published: (2025)
Doubly-Regressing Approach for Subgroup Fairness
by: Kim, Kunwoong, et al.
Published: (2025)
by: Kim, Kunwoong, et al.
Published: (2025)
Identifiable and interpretable nonparametric factor analysis
by: Xu, Maoran, et al.
Published: (2023)
by: Xu, Maoran, et al.
Published: (2023)
Subgroup learning in functional regression models under the RKHS framework
by: Guan, Xin, et al.
Published: (2025)
by: Guan, Xin, et al.
Published: (2025)
Subgroup comparisons within and across studies in meta-analysis
by: Panaro, Renato, et al.
Published: (2025)
by: Panaro, Renato, et al.
Published: (2025)
Data Integration for Estimating Subgroup-Specific Conditional Average Treatment Effects (CATEs) Using Coarsened External Information in Randomized Trials
by: Yang, Youqi, et al.
Published: (2026)
by: Yang, Youqi, et al.
Published: (2026)
Valid and Efficient Two-Stage Latent Subgroup Analysis with Observational Data
by: Luo, Yuanhui, et al.
Published: (2025)
by: Luo, Yuanhui, et al.
Published: (2025)
Constrained Identifiability of Causal Effects
by: Chen, Yizuo, et al.
Published: (2024)
by: Chen, Yizuo, et al.
Published: (2024)
Efficient Active Learning Strategies for Computer Experiments
by: Song, Difan, et al.
Published: (2025)
by: Song, Difan, et al.
Published: (2025)
Adaptive Experiments Toward Learning Treatment Effect Heterogeneity
by: Wei, Waverly, et al.
Published: (2023)
by: Wei, Waverly, et al.
Published: (2023)
Scalable Analysis of Bipartite Experiments
by: Shi, Liang, et al.
Published: (2024)
by: Shi, Liang, et al.
Published: (2024)
Automated Analysis of Experiments using Hierarchical Garrote
by: Yu, Wei-Yang, et al.
Published: (2024)
by: Yu, Wei-Yang, et al.
Published: (2024)
Propensity Score Weighting to Ensure Balance in Key Subgroups or Strata: A Practical Guide
by: Mackay, Emma K., et al.
Published: (2026)
by: Mackay, Emma K., et al.
Published: (2026)
Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE
by: Qiu, Sky, et al.
Published: (2026)
by: Qiu, Sky, et al.
Published: (2026)
When Do Treatment Changes Identify Causal Effects?
by: Huber, Martin
Published: (2026)
by: Huber, Martin
Published: (2026)
Identifying Treatment and Spillover Effects Using Exposure Contrasts
by: Leung, Michael P.
Published: (2024)
by: Leung, Michael P.
Published: (2024)
On the Identifying Power of Generalized Monotonicity for Average Treatment Effects
by: Bai, Yuehao, et al.
Published: (2024)
by: Bai, Yuehao, et al.
Published: (2024)
Towards Learning High-Precision Least Squares Algorithms with Sequence Models
by: Liu, Jerry, et al.
Published: (2025)
by: Liu, Jerry, et al.
Published: (2025)
Minimax Regret Learning for Data with Heterogeneous Subgroups
by: Mo, Weibin, et al.
Published: (2024)
by: Mo, Weibin, et al.
Published: (2024)
Similar Items
-
Adaptive Experimentation When You Can't Experiment
by: Zhao, Yao, et al.
Published: (2024) -
An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects
by: Chiu, Albert
Published: (2025) -
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
by: Fiez, Tanner, et al.
Published: (2024) -
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
by: Xu, Shuoxun, et al.
Published: (2026) -
Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
by: Goplerud, Max, et al.
Published: (2022)