Nonlinear Causal Dose-Response Estimation via Splines R package CausalSpline version 0.1.0 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. Software 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 Subir Hait <haitsubi@msu.edu> Comprehensive R Archive Network (CRAN) Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>) GPL (>= 3) 2026-03-25 application/tgz https://CRAN.R-project.org/package=CausalSpline doi:10.32614/CRAN.package.CausalSpline