# Adaptive Experiment Design with Synthetic Controls

arXiv: https://arxiv.org/abs/2401.17205 · HTML: https://mlsanity.com/paper/2401.17205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 81 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [Best-Arm Identification in Linear Bandits](https://mlsanity.com/paper/1409.6110/) (2014)
- [Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders](https://mlsanity.com/paper/1902.00450/) (2019)
- [Gamification of Pure Exploration for Linear Bandits](https://mlsanity.com/paper/2007.00953/) (2020)
- [An optimal algorithm for the Thresholding Bandit Problem](https://mlsanity.com/paper/1605.08671/) (2016)
- [Fully adaptive algorithm for pure exploration in linear bandits](https://mlsanity.com/paper/1710.05552/) (2017)
- [The True Sample Complexity of Identifying Good Arms](https://mlsanity.com/paper/1906.06594/) (2019)
- [Good Arm Identification via Bandit Feedback](https://mlsanity.com/paper/1710.06360/) (2017)
- [Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints](https://mlsanity.com/paper/2006.05026/) (2020)
- [On the Assumptions of Synthetic Control Methods](https://mlsanity.com/paper/2112.05671/) (2021)
- [Thresholding Bandits with Augmented UCB](https://mlsanity.com/paper/1704.02281/) (2017)
- [Contextual Constrained Learning for Dose-Finding Clinical Trials](https://mlsanity.com/paper/2001.02463/) (2020)
- [Synthetically Controlled Bandits](https://mlsanity.com/paper/2202.07079/) (2022)
- [Thompson Sampling for Combinatorial Network Optimization in Unknown Environments](https://mlsanity.com/paper/1907.04201/) (2019)
- [Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem](https://mlsanity.com/paper/1704.04567/) (2017)
- [Thresholding Bandit with Optimal Aggregate Regret](https://mlsanity.com/paper/1905.11046/) (2019)
- [SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups](https://mlsanity.com/paper/2101.10998/) (2021)
- [Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls](https://mlsanity.com/paper/2112.00278/) (2021)

## Built on by (most cited first, 2 of 2)

- [Defining Expertise: Applications to Treatment Effect Estimation](https://mlsanity.com/paper/2403.00694/) (2024)
- [Treatment, evidence, imitation, and chat](https://mlsanity.com/paper/2506.23040/) (2025)

## Abstract (arXiv)

Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations - especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments.