Adam Glynn <adam.glynn@emory.edu>,
Kevin Quinn <kmq@umich.edu>
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.

Matching on Generalized Propensity Scores with Continuous
Exposures
c(
person("Naeem", "Khoshnevis", email = "nkhoshnevis@g.harvard.edu",
role=c("aut","cre"),
comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION="FASRC")),
person("Xiao", "Wu", email = "wuxiao@g.harvard.edu",
role= c("aut"),
comment = c(ORCID = "0000-0002-4884-657X", AFFILIATION="HSPH")),
person("Danielle","Braun", email = "dbraun@hsph.harvard.edu",
role=c("aut"),
comment = c(ORCID = "0000-0002-5177-8598", AFFILIATION="HSPH")))
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.,
2018. Matching on generalized propensity scores with continuous exposures.
arXiv preprint <arXiv:1812.06575>.
parallel, data.table, SuperLearner, xgboost, earth, ranger,
gam, KernSmooth, MASS, polycor, wCorr, stats, ggplot2, rlang,
logger, glue, Rcpp, gnm, tidyr
Naeem Khoshnevis [aut, cre] (<https://orcid.org/0000-0003-4315-1426>,
FASRC),
Xiao Wu [aut] (<https://orcid.org/0000-0002-4884-657X>, HSPH),
Danielle Braun [aut] (<https://orcid.org/0000-0002-5177-8598>, HSPH)

parallel, data.table, SuperLearner, xgboost, earth, ranger,
gam, KernSmooth, MASS, polycor, wCorr, stats, ggplot2, rlang,
logger, Rcpp, gnm, tidyr

Inferring Causal Effects using Bayesian Structural Time-Series
Models
Kay H. Brodersen <kbrodersen@google.com>,
Alain Hauser <alhauser@google.com>
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.