Implements various estimators for average
  treatment effects - an inverse probability weighted (IPW) estimator, 
  an augmented inverse probability weighted (AIPW) estimator, and a standard
  regression estimator - that make use of generalized additive models for
  the treatment assignment model and/or outcome model. See: Glynn, Adam N.
  and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse
  Propensity Weighted Estimator." Political Analysis. 18: 36-56.

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. (2024) <doi:10.48550/arXiv.2402.04341> for a detailed guide on using the package.