Provides a framework for estimating causal effects of a continuous 
    exposure using observational data, and implementing matching and weighting 
    on the generalized propensity score.
    Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. 
    Matching on generalized propensity scores with continuous exposures. Journal 
    of the American Statistical Association, pp.1-29.

Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA).

Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.

Infers the causal effect of an intervention on a multivariate response through the use of Multivariate 
    Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. 
    The package also includes functions for model building and forecasting.

Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) <doi:10.1017/rsm.2025.5> for a detailed guide on using the package.

The presence of a heavy tail is a feature of many scenarios when risk management
            involves extremely rare events. While parametric distributions may give adequate
            representation of the mode of data, they are likely to misrepresent heavy tails,
            and completely nonparametric approaches lack a rigorous mechanism for
            tail extrapolation; see Pickands (1975) <doi:10.1214/aos/1176343003>. The
            package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed
            outcomes by combining Dirichlet process mixture models for the body of the
            distribution with optional generalized Pareto tails. The method allows
            for unconditional and covariate-modulated mixtures, implements MCMC estimation
            using 'nimble', and extends to mixtures of different arms' outcomes with
            application to causal inference in the Rubin (1974)
            <doi:10.1037/h0037350> framework. Posterior summaries include density
            functions, quantiles, expected values, survival functions, and causal effects,
            with an emphasis on tail quantiles and functional measures sensitive to the
            tail.