# Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population

arXiv: https://arxiv.org/abs/2401.14512 · HTML: https://mlsanity.com/paper/2401.14512/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule](https://mlsanity.com/paper/2003.03977/) (2020)
- [A First Course in Causal Inference](https://mlsanity.com/paper/2305.18793/) (2023)
- [Randomization Inference for Treatment Effect Variation](https://mlsanity.com/paper/1412.5000/) (2014)
- [Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations](https://mlsanity.com/paper/2112.04723/) (2021)
- [Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects](https://mlsanity.com/paper/1610.06943/) (2016)
- [Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/1905.10684/) (2019)
- [Efficient Generalization and Transportation](https://mlsanity.com/paper/2302.00092/) (2023)
- [Global sensitivity analysis for studies extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/2207.09982/) (2022)

## Built on by (most cited first, 6 of 6)

- [Towards Generalizing Inferences from Trials to Target Populations](https://mlsanity.com/paper/2402.17042/) (2024)
- [Regularizing Extrapolation in Causal Inference](https://mlsanity.com/paper/2509.17180/) (2025)
- [Why are there many equally good models? An Anatomy of the Rashomon Effect](https://mlsanity.com/paper/2601.06730/) (2026)
- [A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference](https://mlsanity.com/paper/2505.11014/) (2025)
- [Rate doubly robust estimation for weighted average treatment effects](https://mlsanity.com/paper/2509.14502/) (2025)
- [Mind the Sim-to-Real Gap & Think Like a Scientist](https://mlsanity.com/paper/2605.21458/) (2026)

## Abstract (arXiv)

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 variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial -- investigating the effectiveness of medication for opioid use disorder -- to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations.