HOIF
Higher-Order Influence Function Estimators for the Average Treatment Effect
Implements Higher-Order Influence Function (HOIF) estimators of the Average Treatment Effect (ATE), following Robins et al. (2008) <doi:10.1214/193940307000000527>, Liu et al. (2017) <doi:10.48550/arXiv.1705.07577> and Liu and Li (2023) <doi:10.48550/arXiv.2302.08097>. Estimators of any order are supported, with optional covariate basis transformations (B-splines, Fourier) and optional K-fold sample splitting (cross-fitting) for improved finite-sample performance. The core higher-order U-statistics are computed exactly via the 'ustats' package, an R interface to the 'Python' package 'u-stats'; the underlying algorithm and its computational complexity are analyzed in Chen, Zhang and Liu (2025) <doi:10.48550/arXiv.2508.12627>. A pure R implementation (up to order 6) is also provided as a fallback that does not require 'Python'.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | HOIF_0.2.0.tar.gz |
328.0 KiB |
0.2.0 |
rolling linux/noble R-4.5 | HOIF_0.2.0.tar.gz |
327.7 KiB |
0.2.0 |
rolling source/ R- | HOIF_0.2.0.tar.gz |
272.1 KiB |
0.2.0 |
latest linux/jammy R-4.5 | HOIF_0.2.0.tar.gz |
328.0 KiB |
0.2.0 |
latest linux/noble R-4.5 | HOIF_0.2.0.tar.gz |
327.7 KiB |
0.2.0 |
latest source/ R- | HOIF_0.2.0.tar.gz |
272.1 KiB |
0.2.0 |
2026-04-23 source/ R- | HOIF_0.2.0.tar.gz |
0 B |