# Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials

arXiv: https://arxiv.org/abs/2403.03058 · HTML: https://mlsanity.com/paper/2403.03058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [High-dimensional regression adjustments in randomized experiments](https://mlsanity.com/paper/1607.06801/) (2016)
- [Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials](https://mlsanity.com/paper/2009.11828/) (2020)

## Abstract (arXiv)

In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum's framework of exact tests in randomized experiments with covariate adjustments. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially when nonlinear association or interaction among covariates is expected. Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.