# DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments

arXiv: https://arxiv.org/abs/2605.06608 · HTML: https://mlsanity.com/paper/2605.06608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 12 of 12)

- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [Active Feature Acquisition with Generative Surrogate Models](https://mlsanity.com/paper/2010.02433/) (2020)
- [Classification with Costly Features using Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07364/) (2017)
- [An Analysis of Ensemble Sampling](https://mlsanity.com/paper/2203.01303/) (2022)
- [Variable Selection via Thompson Sampling](https://mlsanity.com/paper/2007.00187/) (2020)
- [Budgeted Combinatorial Multi-Armed Bandits](https://mlsanity.com/paper/2202.03704/) (2022)
- [Rerandomization and Regression Adjustment](https://mlsanity.com/paper/1906.11291/) (2019)
- [Variable Importance Matching for Causal Inference](https://mlsanity.com/paper/2302.11715/) (2023)
- [Anytime-Valid Inference in Adaptive Experiments: Covariate Adjustment and Balanced Power](https://mlsanity.com/paper/2506.20523/) (2025)
- [Design-based theory for causal inference from adaptive experiments](https://mlsanity.com/paper/2602.21998/) (2026)

## Abstract (arXiv)

Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, forcing a trade-off between covariate-adaptive precision and a measurement budget. We introduce Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS), which treats covariate acquisition as a sequential optimization problem embedded within a design-based causal inference task. A budgeted combinatorial Thompson sampler learns which covariates are most prognostic across successive batches; selected covariates then drive rerandomization and regression adjustment to reduce batch-level average treatment effect variance. Our primary theoretical contribution is a decoupling result: adaptive covariate selection based on past batches preserves batch-level randomization validity, and the cumulative inverse-variance weighted estimator achieves at least nominal asymptotic coverage. We further derive a Bayes risk bound for the acquisition layer that matches the minimax lower bound up to logarithmic factors. Empirically, DARTS systematically concentrates the budget on informative features, significantly closing the efficiency gap to oracle designs while maintaining strict inferential validity.