CausalGAM: Estimation of Causal Effects with Generalized Additive Models

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This package 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.

Author
Adam Glynn <aglynn@iq.harvard.edu>, Kevin Quinn <kquinn@law.berkeley.edu>
Date of publication
2010-02-11 08:44:51
Maintainer
Kevin Quinn <kquinn@law.berkeley.edu>
License
GPL-2
Version
0.1-3

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Man pages

balance.IPW
Check Post-Weighting Balance for (A)IPW Estimators Using...
estimate.ATE
Estimate Population Average Treatment Effects (ATE) Using...

Files in this package

CausalGAM
CausalGAM/COPYING
CausalGAM/DESCRIPTION
CausalGAM/HISTORY
CausalGAM/LICENSE
CausalGAM/man
CausalGAM/man/balance.IPW.Rd
CausalGAM/man/estimate.ATE.Rd
CausalGAM/NAMESPACE
CausalGAM/R
CausalGAM/R/balanceIPW.R
CausalGAM/R/CausalGAM.R
CausalGAM/R/zzz.R
CausalGAM/README