Naeem Khoshnevis, Xiao Wu, Danielle Braun
arXiv 1 Oct 2023 · Statistics — Computation · 3 citations (OpenAlex)
arXiv:2310.00561 · PDF · DOI · OpenAlex · Extracted main text
Quantifying the causal effects of continuous exposures on outcomes of interest is critical for social, economic, health, and medical research. However, most existing software packages focus on binary exposures. We develop the CausalGPS R package that implements a collection of algorithms to provide algorithmic solutions for causal inference with continuous exposures. CausalGPS implements a causal inference workflow, with algorithms based on generalized propensity scores (GPS) as the core, extending propensity scores (the probability of a unit being exposed given pre-exposure covariates) from binary to continuous exposures. As the first step, the package implements efficient and flexible estimations of the GPS, allowing multiple user-specified modeling options. As the second step, the package provides two ways to adjust for confounding: weighting and matching, generating weighted and matched data sets, respectively. Lastly, the package provides built-in functions to fit flexible parametric, semi-parametric, or non-parametric regression models on the weighted or matched data to estimate the exposure-response function relating the outcome with the exposures. The computationally intensive tasks are implemented in C++, and efficient shared-memory parallelization is achieved by OpenMP API. This paper outlines the main components of the CausalGPS R package and demonstrates its application to assess the effect of long-term exposure to PM2.5 on educational attainment using zip code-level data from the contiguous United States from 2000-2016.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wu X, Mealli F, Kioumourtzoglou MA, Dominici F, Braun D (2022) Matching on generalized propensity scores with continuous exposures | 1.000 | 13 | 4 | 100% |
| 2 | Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology | 0.874 | 5 | 2 | 100% |
| 3 | R Core Team (2023) R: A Language and Environment for Statistical Computing | 0.737 | 3 | 2 | 100% |
| 4 | Kennedy EH, Ma Z, McHugh MD, Small DS (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects | 0.737 | 3 | 2 | 100% |
| 5 | Austin PC (2018) Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures | 0.585 | 3 | 1 | 100% |
| 6 | Harder VS, Stuart EA, Anthony JC (2010) Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research | 0.585 | 3 | 1 | 100% |
| 7 | Zhu Y, Coffman DL, Ghosh D (2015) A boosting algorithm for estimating generalized propensity scores with continuous treatments | 0.585 | 3 | 1 | 100% |
| fong2018covariate | unmatched citation key fong2018covariate | 0.511 | 2 | 1 | 100% |
| 9 | Imbens GW, Rubin DB (2015) Causal inference in statistics, social, and biomedical sciences | 0.511 | 2 | 1 | 100% |
| 10 | Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 36 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.