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>.

Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.

Provides a unified workflow for running, classifying, visualizing,
    and interpreting sensitivity analyses for unmeasured confounding across
    multiple causal frameworks. Introduces the Causal Fragility Index (CFI),
    a single 0-100 composite score that integrates evidence from the partial
    R-squared robustness value approach (Cinelli and Hazlett, 2020,
    <doi:10.1111/rssb.12348>), E-value metrics (VanderWeele and Ding, 2017,
    <doi:10.7326/M16-2607>), and the Impact Threshold for a Confounding
    Variable (Frank, 2000, <doi:10.1177/0049124100029002001>) into one
    interpretable measure of robustness. The package also provides
    template-based plain-language narrative interpretation and
    publication-ready reporting, with optional integration with the
    'confoundvis' package for sensitivity plots.