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> and Robertson et al. (2021) <arXiv:2104.05905>. 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.

Provides an array of statistical models common in causal inference such as 
  standardization, IP weighting, propensity matching, outcome regression, and doubly-robust 
  estimators. Estimates of the average treatment effects from each model are given with the 
  standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).

When causal quantities are not identifiable from the observed data, it still may be possible 
            to bound these quantities using the observed data. We outline a class of problems for which the 
            derivation of tight bounds is always a linear programming problem and can therefore, at least 
            theoretically, be solved using a symbolic linear optimizer. We extend and generalize the 
            approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide 
            a user friendly graphical interface for setting up such problems via directed acyclic 
            graphs (DAG), which only allow for problems within this class to be depicted. The user can 
            then define linear constraints to further refine their assumptions to meet their specific 
            problem, and then specify a causal query using a text interface. The program converts this 
            user defined DAG, query, and constraints, and returns tight bounds. The bounds can be 
            converted to R functions to evaluate them for specific datasets, and to latex code for 
            publication. The methods and proofs of tightness and validity of the bounds are described in
            a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) 
            <doi:10.1080/10618600.2022.2071905>.