Uses optimal transport distances to find probabilistic 
    matching estimators for causal inference.
    These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>.
    The package will build the weights, estimate treatment effects, and
    calculate confidence intervals via the methods described in the paper.
    The package also supports several other methods as described in the help 
    files.

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.