Calculates population attributable fraction causal effects.
    The 'causalPAF' package contains a suite of functions for causal
    analysis calculations of population attributable fractions (PAF) given
    a causal diagram which apply both: Pathway-specific population
    attributable fractions (PS-PAFs) O’Connell and Ferguson (2022)
    <doi:10.1093/ije/dyac079> and Sequential population attributable
    fractions Ferguson, O’Connell, and O’Donnell (2020)
    <doi:10.1186/s13690-020-00442-x>.  Results are presentable in both
    table and plot format.

Implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable 
    estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, 
    regression discontinuity, and difference-in-differences designs.

Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries.  Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).

An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents.

The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.

Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020)  <doi:10.1002/jae.2765>, and others.