FPScausal-package: FPScausal: Causal Inference with Functional Propensity Score...
In FPScausal: Functional Propensity Score for Causal Inference

FPScausal-package

R Documentation

FPScausal: Causal Inference with Functional Propensity Score Weighting

Description

FPScausal implements the functional propensity score (FPS) weighting methodology for causal inference with functional treatments. Given a functional treatment X(t), a set of scalar and/or functional confounders, and an outcome (continuous, binary, or functional), the package:

1. Decomposes X(t) into Functional Principal Component (FPC) scores via fda. 2. Estimates covariate-balancing weights by maximising the empirical likelihood subject to balancing constraints and solving the resulting dual problem via the BFGS quasi-Newton algorithm. 3. Estimates the causal effect function \mu(t) (scalar/binary outcome) or causal effect surface \mu(s,t) (functional outcome) via weighted least squares. 4. Provides pointwise bootstrap confidence intervals and a rich set of diagnostic and result plots.

Main functions

fps_weighting: Estimate FPS weights.
fps_effect_estimation: Estimate causal effects using FPS weights.
simulate_fps_data: Generate synthetic datasets for testing and vignette illustrations.

S3 methods

Both fps_weighting and fps_effect_estimation objects support print, summary, and plot with a type argument.

References

Ciardulli, S. and Fontana, N. (2026). Functional propensity score weighting for causal inference with functional treatments, covariates, and outcomes. Preprint.