Papers › fastrerandomize: Fast Rerandomization Using Accelerated Computing
fastrerandomize: Fast Rerandomization Using Accelerated Computing
Rebecca Goldstein, Connor T. Jerzak, Aniket Kamat, Fucheng Warren Zhu
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We present fastrerandomize, an R package for fast, scalable rerandomization in experimental design. Rerandomization improves precision by discarding treatment assignments that fail a prespecified covariate-balance criterion, but existing implementations can become computationally prohibitive as the number of units or covariates grows. fastrerandomize introduces three complementary advances: (i) optional GPU/TPU acceleration to parallelize balance checks, (ii) memory-efficient key-only storage that avoids retaining full assignment matrices, and (iii) auto-vectorized, just-in-time compiled kernels for batched candidate generation and inference. This approach enables exact or Monte Carlo rerandomization at previously intractable scales, making it practical to adopt the tighter balance thresholds required in modern high-dimensional experiments while simultaneously quantifying the resulting gains in precision and power for a given covariate set. Our approach also supports randomization-based testing conditioned on acceptance. In controlled benchmarks, we observe order-of-magnitude speedups over baseline workflows, with larger gains as the sample size or dimensionality grows, translating into improved precision of causal estimates.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections