Learn R Programming

⚠️There's a newer version (1.1.3) of this package.Take me there.

causalweight (version 1.0.2)

Estimation Methods for Causal Inference Based on Inverse Probability Weighting

Description

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) , Huber (2012) , Huber (2014) , Huber (2014) , Froelich and Huber (2017) , Hsu, Huber, Lee, and Lettry (2020) , and others.

Copy Link

Version

Install

install.packages('causalweight')

Monthly Downloads

962

Version

1.0.2

License

MIT + file LICENSE

Maintainer

Hugo Bodory

Last Published

July 8th, 2021

Functions in causalweight (1.0.2)

Instrument-based treatment evaluation under endogeneity and non-response bias

Causal mediation analysis with double machine learning

Dynamic treatment effect evaluation with double machine learning

Sharp regression discontinuity design conditional on covariates

Difference-in-differences based on inverse probability weighting

Local average treatment effect estimation based on inverse probability weighting

Local average treatment effect estimation in multiple follow-up periods with outcome attrition based on inverse probability weighting

Sales of video games

Information leaflet on coffee production and environmental awareness of high school / university students in Bulgaria

Causal mediation analysis with a continuous treatment based on weighting by the inverse of generalized propensity scores

Causal mediation analysis based on inverse probability weighting with optional sample selection correction.

Causal mediation analysis with instruments for treatment and mediator based on weighting

Correspondence test in Swiss apprenticeship market

Binary or multiple treatment effect evaluation with double machine learning under sample selection/outcome attrition

Binary or multiple discrete treatment effect evaluation with double machine learning

Wage expectations of students in Switzerland

Austrian unemployment duration data

Treatment evaluation based on inverse probability weighting with optional sample selection correction.