We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (<doi:10.1177/00491241211067516>; <doi:10.1177/00811750231183711>). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 <doi:10.1515/jci-2022-0031>). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) <doi:10.48550/arXiv.2506.19010>.

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.

Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.  A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.

Causal Distillation Tree (CDT) is a novel machine learning method 
    for estimating interpretable subgroups with heterogeneous treatment effects. 
    CDT allows researchers to fit any machine learning model (or metalearner) to 
    estimate heterogeneous treatment effects for each individual, and then 
    "distills" these predicted heterogeneous treatment effects into 
    interpretable subgroups by fitting an ordinary decision tree to predict the
    previously-estimated heterogeneous treatment effects. This package 
    provides tools to estimate causal distillation trees (CDT), as detailed in
    Huang, Tang, and Kenney (2025) <doi:10.48550/arXiv.2502.07275>.