# Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence

arXiv: https://arxiv.org/abs/2602.11333 · HTML: https://mlsanity.com/paper/2602.11333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 11 Feb 2026.

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

- [Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks](https://mlsanity.com/paper/1703.02930/) (2017)
- [Applied Causal Inference Powered by ML and AI](https://mlsanity.com/paper/2403.02467/) (2024)
- [Debiased Machine Learning without Sample-Splitting for Stable Estimators](https://mlsanity.com/paper/2206.01825/) (2022)
- [Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework](https://mlsanity.com/paper/1512.07619/) (2015)
- [Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems](https://mlsanity.com/paper/1304.0282/) (2013)
- [Inference in High Dimensional Panel Models with an Application to Gun Control](https://mlsanity.com/paper/1411.6507/) (2014)
- [Jackknife multiplier bootstrap: finite sample approximations to the U-process supremum with applications](https://mlsanity.com/paper/1708.02705/) (2017)
- [Multiway empirical likelihood](https://mlsanity.com/paper/2108.04852/) (2021)
- [Generalization Properties of Decision Trees on Real-valued and Categorical Features](https://mlsanity.com/paper/2210.10781/) (2022)
- [Estimation and Inference for Causal Functions with Multiway Clustered Data](https://mlsanity.com/paper/2409.06654/) (2024)
- [Analytic inference with two-way clustering](https://mlsanity.com/paper/2506.20749/) (2025)
- [Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data](https://mlsanity.com/paper/2511.10995/) (2025)

## Abstract (arXiv)

This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. While cross-fitting is commonly employed, it can be statistically inefficient and computationally burdensome when first-stage learners are complex and the effective sample size is governed by the number of independent clusters. We show that valid inference can be achieved without sample splitting by combining orthogonal moments with a localisation-based empirical process approach, allowing for an arbitrary number of clustering dimensions. The resulting debiased GMM estimators are shown to be asymptotically linear and asymptotically normal under multiway clustered dependence. A central technical contribution of the paper is the derivation of novel global and local maximal inequalities for empirical processes indexed by general classes of functions for separately exchangeable arrays, which underpin our theoretical arguments and are of independent interest. Python and R packages are available for implementing the proposed procedures.