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causalweight (version 1.1.3)

Estimation Methods for Causal Inference Based on Inverse Probability Weighting and Doubly Robust Estimation

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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Install

install.packages('causalweight')

Monthly Downloads

1,151

Version

1.1.3

License

MIT + file LICENSE

Maintainer

Hugo Bodory

Last Published

March 26th, 2025

Functions in causalweight (1.1.3)

identificationDML

Testing identification with double machine learning

Local average treatment effect estimation based on inverse probability weighting

didcontDMLpanel

Continuous Difference-in-Differences using Double Machine Learning for Panel Data

Causal mediation analysis with double machine learning

India's National Health Insurance Program (RSBY)

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

Difference-in-differences based on inverse probability weighting

Temporary Work Agency (TWA) Assignments and Permanent Employment in Sicily

Dynamic treatment effect evaluation with double machine learning

Sales of video games

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

Swedish municipalities

Test for identification in causal mediation and dynamic treatment models

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

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

paneltestDML: Overidentification test for ATET estimation in panel data

Binary or multiple discrete treatment effect evaluation with double machine learning

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

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

Correspondence test in Swiss apprenticeship market

Wage expectations of students in Switzerland

Austrian unemployment duration data

ATET Estimation for Binary Treatments using Double Machine Learning

Difference-in-Differences in Repeated Cross-Sections for Binary Treatments using Double Machine Learning

Sharp regression discontinuity design conditional on covariates

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

Continuous Difference-in-Differences using Double Machine Learning for Repeated Cross-Sections

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

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.

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.

Expenditure and Default Data