Causal Inference Modeling for Estimation of Causal Effects
c(person(given = "Joshua",
family = "Anderson",
role = c("aut", "cre", "cph"),
email = "jwanderson198@gmail.com"),
person(given = "Cyril",
family = "Rakovski",
role = "rev",
email = "rakovski@chapman.edu"),
person(given = "Yesha",
family = "Patel",
role = "rev"),
person(given = "Erin",
family = "Lee",
role = "rev"))
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/>).
Joshua Anderson [aut, cre, cph],
Cyril Rakovski [rev],
Yesha Patel [rev],
Erin Lee [rev]

stats, causaldata, boot, multcomp, geepack

Make, Update, and Query Binary Causal Models
c(person("Clara", "Bicalho", email = "clarabmcorreia@gmail.com", role = c("ctb")),
person("Jasper", "Cooper", email = "jjc2247@columbia.edu", role = c("ctb")),
person("Macartan", "Humphreys", email = "mh2245@columbia.edu", role = c("aut")),
person("Alan", "Jacobs", email = "alan.jacobs@ubc.ca", role = c("aut")),
person("Merlin", "Heidemanns", email = "mnh2123@columbia.edu ", role = c("ctb")),
person("Lily", "Medina", email = "lilymiru@gmail.com", role = c("aut" , "cre")),
person("Julio", "Solis", email = "juliosolisar@gmail.com", role = c("ctb")),
person("Georgiy", "Syunyaev", email = "gs2716@columbia.edu", role = c("ctb")))
Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands.  Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).
dagitty, gtools, stats, randomizr, rlang (>= 0.2.0), rstan (>=
2.18.1), rstantools (>= 2.0.0), stringr
testthat, rmarkdown, knitr, DeclareDesign, covr
Clara Bicalho [ctb],
Jasper Cooper [ctb],
Macartan Humphreys [aut],
Alan Jacobs [aut],
Merlin Heidemanns [ctb],
Lily Medina [aut, cre],
Julio Solis [ctb],
Georgiy Syunyaev [ctb]