CausalSpline: Nonlinear Causal Dose-Response Estimation via Splines
Estimates nonlinear causal dose-response functions for continuous
treatments using spline-based methods under standard causal assumptions
(unconfoundedness / ignorability). Implements three identification
strategies: Inverse Probability Weighting (IPW) via the generalised
propensity score (GPS), G-computation (outcome regression), and a
doubly-robust combination. Natural cubic splines and B-splines are
supported for both the exposure-response curve f(T) and the propensity
nuisance model. Pointwise confidence bands are obtained via the sandwich
estimator or nonparametric bootstrap. Also provides fragility diagnostics
including pointwise curvature-based fragility, uncertainty-normalised
fragility, and regional integration over user-defined treatment intervals.
Builds on the framework of Hirano and Imbens (2004)
<doi:10.1111/j.1468-0262.2004.00481.x> for continuous treatments and
extends it to fully nonparametric spline estimation.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
splines, stats, utils, ggplot2 (≥ 3.4.0), sandwich, boot |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, patchwork, cobalt, dplyr |
| Published: |
2026-03-25 |
| DOI: |
10.32614/CRAN.package.CausalSpline (may not be active yet) |
| Author: |
Subir Hait  [aut,
cre] |
| Maintainer: |
Subir Hait <haitsubi at msu.edu> |
| BugReports: |
https://github.com/causalfragility-lab/CausalSpline/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/causalfragility-lab/CausalSpline |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
CausalSpline results |
Documentation:
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