CRAN/E | causalweight

causalweight

Estimation Methods for Causal Inference Based on Inverse Probability Weighting

Installation

About

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) doi:10.1016/j.jeconom.2006.06.004, Huber (2012) doi:10.3102/1076998611411917, Huber (2014) doi:10.1080/07474938.2013.806197, Huber (2014) doi:10.1002/jae.2341, Froelich and Huber (2017) doi:10.1111/rssb.12232, Hsu, Huber, Lee, and Lettry (2020) doi:10.1002/jae.2765, and others.

Key Metrics

Version 1.1.0
R ≥ 3.5.0
Published 2024-01-24 93 days ago
Needs compilation? no
License MIT
License File
CRAN checks causalweight results

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Maintainer

Maintainer

Hugo Bodory

hugo.bodory@unisg.ch

Authors

Hugo Bodory

aut / cre

Martin Huber

aut

Jannis Kueck

aut

Material

Reference manual
Package source

In Views

CausalInference

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

causalweight archive

Depends

R ≥ 3.5.0
ranger

Imports

mvtnorm
np
LARF
hdm
SuperLearner
glmnet
xgboost
e1071
fastDummies
grf
checkmate