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  "Title": "Nonlinear Causal Dose-Response Estimation via Splines",
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  "Description": "Estimates nonlinear causal dose-response functions for\ncontinuous treatments using spline-based methods under standard\ncausal assumptions (unconfoundedness / ignorability).\nImplements three identification strategies: Inverse Probability\nWeighting (IPW) via the generalised propensity score (GPS),\nG-computation (outcome regression), and a doubly-robust\ncombination. Natural cubic splines and B-splines are supported\nfor both the exposure-response curve f(T) and the propensity\nnuisance model. Pointwise confidence bands are obtained via the\nsandwich estimator or nonparametric bootstrap. Also provides\nfragility diagnostics including pointwise curvature-based\nfragility, uncertainty-normalised fragility, and regional\nintegration over user-defined treatment intervals. Builds on\nthe framework of Hirano and Imbens (2004)\n<doi:10.1111/j.1468-0262.2004.00481.x> for continuous\ntreatments and extends it to fully nonparametric spline\nestimation.",
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      "title": "CausalSpline: Nonlinear Causal Dose-Response Estimation via Splines",
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        "CausalSpline"
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      "title": "Nonlinear causal dose-response estimation via splines",
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      "title": "Extract the dose-response curve data frame",
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