We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).

Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <arXiv:2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.

Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.

Example data sets to run the example
    problems from causal inference textbooks. Currently, contains data
    sets for Huntington-Klein, Nick (2021) "The Effect" <https://theeffectbook.net>,
    Cunningham, Scott (2021, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", 
    and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" 
    <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.

Various tools for inferring causal models from observational data. The package 
    includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler 
    and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools
    for evaluating differences in adjacency matrices, which can be used for evaluating
    performance of causal discovery procedures.