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causalweight (version 1.1.2)

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.

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Version

Install

install.packages('causalweight')

Monthly Downloads

709

Version

1.1.2

License

MIT + file LICENSE

Maintainer

Hugo Bodory

Last Published

March 22nd, 2025

Functions in causalweight (1.1.2)

Local average treatment effect estimation based on inverse probability weighting

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

Correspondence test in Swiss apprenticeship market

Causal mediation analysis with double machine learning

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

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

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

India's National Health Insurance Program (RSBY)

Swedish municipalities

Binary or multiple discrete treatment effect evaluation with double machine learning

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

Wage expectations of students in Switzerland

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

Austrian unemployment duration data

Sharp regression discontinuity design conditional on covariates

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

identificationDML

Testing identification with double machine learning

Difference-in-differences based on inverse probability weighting

Dynamic treatment effect evaluation with double machine learning

couponsretailer

Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.

Data on daily spending and coupon receipt (selective subsample) This data set is a selective subsample of the data set "couponsretailer" which was constructed for illustrative purposes.

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

Sales of video games