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Quantifying the causal effects of continuous exposures on outcomes ofinterest is critical for social, economic, health, and medical research.However, most existing software packages focus on binary exposures. We developthe CausalGPS R package that implements a collection of algorithms to providealgorithmic solutions for causal inference with continuous exposures. CausalGPSimplements a causal inference workflow, with algorithms based on generalizedpropensity scores (GPS) as the core, extending propensity scores (theprobability of a unit being exposed given pre-exposure covariates) from binaryto continuous exposures. As the first step, the package implements efficientand flexible estimations of the GPS, allowing multiple user-specified modelingoptions. As the second step, the package provides two ways to adjust forconfounding: weighting and matching, generating weighted and matched data sets,respectively. Lastly, the package provides built-in functions to fit flexibleparametric, semi-parametric, or non-parametric regression models on theweighted or matched data to estimate the exposure-response function relatingthe outcome with the exposures. The computationally intensive tasks areimplemented in C++, and efficient shared-memory parallelization is achieved byOpenMP API. This paper outlines the main components of the CausalGPS R packageand demonstrates its application to assess the effect of long-term exposure toPM2.5 on educational attainment using zip code-level data from the contiguousUnited States from 2000-2016.

CausalGPS: An R Package for Causal Inference With Continuous Exposures