crossfit: A Graph-Based Cross-Fitting Engine in R

2026-05-15

AI Summary

This work addresses the challenges of achieving efficient semiparametric estimation of low-dimensional target parameters in the presence of high-dimensional nuisance functions, where out-of-sample prediction of nuisances and statistical efficiency are critical concerns. The authors propose a general, estimator-agnostic cross-fitting engine that automatically executes reproducible folding schedules based on user-specified target functionals and a directed acyclic graph (DAG) encoding the nuisance model structure. Innovatively leveraging graph-based nuisance modeling, the framework supports folding strategies such as disjointness and independence enhancement, while reducing branching dependencies through node replication. It further incorporates explicit scheduling, dependency validation, intelligent caching, and fault isolation mechanisms. Implemented as a lightweight R package publicly available on CRAN, this approach substantially improves the controllability, reproducibility, and computational efficiency of cross-fitting estimators, facilitating large-scale simulations and rapid prototyping.

Abstract

Cross-fitting is a key ingredient in many semiparametric estimation procedures, such as double/debiased machine learning (DML), enabling valid estimation of low-dimensional targets in the presence of high-dimensional nuisance functions by enforcing out-of-sample use of nuisance predictions. crossfit is an R package that provides a general-purpose, estimator-agnostic cross-fitting engine. Users specify (i) a target functional and (ii) a directed acyclic graph (DAG) of nuisance models, with node-specific training fold widths and target-specific evaluation windows. The engine executes a reproducible schedule over folds, panels, and repetitions, returning either a scalar estimate (mode="estimate") or a cross-fitted predictor function for application to new data (mode="predict"). Beyond standard cross-fitting, crossfit implements fold-allocation modes that control how training data are shared across nuisance components, including disjoint and independence-enforcing allocations that duplicate reused nodes to reduce dependence between nuisance branches. The implementation targets simulation-heavy benchmarking and method development, with explicit and auditable schedules, defensive validation of specifications and nuisance dependencies, reuse-aware caching to avoid redundant refits, and failure isolation policies for large experiment grids. The crossfit package is available on CRAN, openly developed on GitHub under GPL-3, and is intended as a lightweight, tested foundation to prototype and empirically evaluate cross-fitted estimators with explicit control over fold geometry, dependence, and computation.

Problem

• cross-fitting
• semiparametric estimation
• nuisance functions
• double machine learning
• fold allocation

Innovation

• cross-fitting
• directed acyclic graph
• double machine learning
• nuisance models
• fold allocation