# Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting

arXiv: https://arxiv.org/abs/2605.09116 · HTML: https://mlsanity.com/paper/2605.09116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 9 May 2026.

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

- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Phase Retrieval via Wirtinger Flow: Theory and Algorithms](https://mlsanity.com/paper/1407.1065/) (2014)
- [Time series experiments and causal estimands: exact randomization tests and trading](https://mlsanity.com/paper/1706.07840/) (2017)
- [What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature](https://mlsanity.com/paper/2201.01194/) (2022)
- [Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption](https://mlsanity.com/paper/1808.05293/) (2018)
- [Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective](https://mlsanity.com/paper/2003.09915/) (2020)
- [No-harm calibration for generalized Oaxaca-Blinder estimators](https://mlsanity.com/paper/2012.09246/) (2020)
- [The Generalized Oaxaca-Blinder Estimator](https://mlsanity.com/paper/2004.11615/) (2020)
- [A conditional randomization test to account for covariate imbalance in randomized experiments](https://mlsanity.com/paper/1510.06817/) (2015)
- [Covariate-adjusted Fisher randomization tests for the average treatment effect](https://mlsanity.com/paper/2010.14555/) (2020)
- [ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments](https://mlsanity.com/paper/2501.07722/) (2025)
- [Adaptive sample splitting for randomization tests](https://mlsanity.com/paper/2504.21572/) (2025)
- [Unifying regression-based and design-based causal inference in time-series experiments](https://mlsanity.com/paper/2510.22864/) (2025)

## Abstract (arXiv)

Randomization tests and flexible treatment-effect models offer complementary strengths for analyzing data from randomized panel experiments: the former provide valid inference under the known assignment mechanism, while the latter can capture complex patterns of effect heterogeneity. We develop model-assisted randomization tests that combine these strengths without sample splitting. The key idea is to estimate an unsigned version of the conditional average treatment effect (CATE) from the covariance structure of residualized outcomes, while leaving the realized assignments for randomization inference. The remaining sign can be chosen to best fit the observed outcomes. We establish identification and consistency for the proposed unsigned CATE estimators, as well as validity for the CATE-assisted randomization tests. Across synthetic and semi-synthetic experiments, the CATE-assisted randomization tests control Type I error and achieve higher power than covariate-adjusted and sample-split alternatives. Finally, we show that the assignment-free CATE estimates can be used to discover heterogeneous subgroups and test subgroup-specific treatment effects.