March 8, 2024, 5:43 a.m. | Harsh Parikh, Rachael Ross, Elizabeth Stuart, Kara Rudolph

cs.LG updates on arXiv.org arxiv.org

arXiv:2401.14512v2 Announce Type: replace-cross
Abstract: Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the …

abstract arxiv challenges cs.cy cs.lg effects inferences issue novel paper population serve stat.ap stat.me subgroups type underrepresentation understanding

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Director, Clinical Data Science

@ Aura | Remote USA

Research Scientist, AI (PhD)

@ Meta | Menlo Park, CA | New York City