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CausalSpline  

Nonlinear Causal Dose-Response Estimation via Splines
View on CRAN: Click here


Download and install CausalSpline package within the R console
Install from CRAN:
install.packages("CausalSpline")

Install from Github:
library("remotes")
install_github("cran/CausalSpline")

Install by package version:
library("remotes")
install_version("CausalSpline", "0.1.0")



Attach the package and use:
library("CausalSpline")
Maintained by
Subir Hait
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-25
Latest Update: 2026-03-25
Description:
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.
How to cite:
Subir Hait (2026). CausalSpline: Nonlinear Causal Dose-Response Estimation via Splines. R package version 0.1.0, https://cran.r-project.org/web/packages/CausalSpline. Accessed 24 Oct. 2026.
Previous versions and publish date:
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Complete documentation for CausalSpline
Functions, R codes and Examples using the CausalSpline R package
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