Why nonlinear causal effects? | Installation | Quick start | 1. Simulate data with a threshold effect | 2. Fit with IPW | 3. Fit with G-computation | 4. Check overlap (positivity) | Comparing DGPs | Choosing degrees of freedom | Methods summary | References
Package: CausalSpline 0.1.0
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.
Authors:
CausalSpline_0.1.0.tar.gz
CausalSpline_0.1.0.zip(r-4.7-any)CausalSpline_0.1.0.zip(r-4.6-any)CausalSpline_0.1.0.zip(r-4.5-any)
CausalSpline_0.1.0.tgz(r-4.6-any)CausalSpline_0.1.0.tgz(r-4.5-any)
CausalSpline_0.1.0.tar.gz(r-4.7-any)CausalSpline_0.1.0.tar.gz(r-4.6-any)
CausalSpline_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CausalSpline/json (API)
| # Install 'CausalSpline' in R: |
| install.packages('CausalSpline', repos = c('https://causalfragility-lab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/causalfragility-lab/causalspline/issues
Last updated from:d3522dc074. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 147 | ||
| source / vignettes | OK | 212 | ||
| linux-release-x86_64 | OK | 137 | ||
| macos-release-arm64 | OK | 102 | ||
| macos-oldrel-arm64 | OK | 75 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 81 | ||
| windows-oldrel | OK | 95 | ||
| wasm-release | OK | 94 |
Exports:causal_splinecheck_overlapdose_response_curvefragility_curvegps_modelgradient_curveregion_fragilitysimulate_dose_responsetrim_weights
Dependencies:bootclicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleR6RColorBrewerrlangS7sandwichscalesvctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| CausalSpline: Nonlinear Causal Dose-Response Estimation via Splines | CausalSpline-package CausalSpline |
| Nonlinear causal dose-response estimation via splines | causal_spline |
| Diagnose positivity / overlap for a continuous treatment | check_overlap |
| Extract the dose-response curve data frame | dose_response_curve |
| Geometric fragility curve for a CausalSpline fit | fragility_curve |
| Fit the Generalised Propensity Score model | gps_model |
| Numerical derivatives of a CausalSpline dose-response curve | gradient_curve |
| Plot the estimated dose-response curve | plot.causal_spline |
| Plot method for fragility_curve objects | plot.fragility_curve |
| Predict E[Y(t)] at new treatment values | predict.causal_spline |
| Print method for causal_spline objects | print.causal_spline |
| Regional fragility summary for a CausalSpline fit | region_fragility |
| Simulate nonlinear dose-response data | simulate_dose_response |
| Summary method for causal_spline objects | summary.causal_spline |
| Trim extreme IPW weights | trim_weights |
