CausalGAM: Estimation of Causal Effects with Generalized Additive Models

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.

Version: 0.1-4
Depends: R (≥ 2.9.0), gam (≥ 1.0.1)
Published: 2017-10-19
Author: Adam Glynn, Kevin Quinn
Maintainer: Kevin Quinn <kmq at umich.edu>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: CausalGAM results

Downloads:

Reference manual: CausalGAM.pdf
Package source: CausalGAM_0.1-4.tar.gz
Windows binaries: r-devel: CausalGAM_0.1-4.zip, r-release: CausalGAM_0.1-4.zip, r-oldrel: CausalGAM_0.1-4.zip
OS X El Capitan binaries: r-release: CausalGAM_0.1-4.tgz
OS X Mavericks binaries: r-oldrel: CausalGAM_0.1-4.tgz
Old sources: CausalGAM archive

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