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causalweight (version 1.1.6)

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 based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs).

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install.packages('causalweight')

Monthly Downloads

1,728

Version

1.1.6

License

MIT + file LICENSE

Maintainer

Hugo Bodory

Last Published

September 25th, 2026

Functions in causalweight (1.1.6)

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

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

Local average treatment effect estimation based on inverse probability weighting

Natural direct and indirect quantile treatment effects with double machine learning

Causal mediation analysis with double machine learning

paneltestDML: Overidentification test for ATET estimation in panel data

Test for identification in causal mediation and dynamic treatment models

Binary or multiple discrete treatment effect evaluation with double machine learning

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

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

Continuous Treatment Effect Estimation under Selection on Observables using Double Machine Learning

Wage expectations of students in Switzerland

Swedish municipalities

India's National Health Insurance Program (RSBY)

predict.cw_superlearner

Predictions from the internal nuisance learner

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

Correspondence test in Swiss apprenticeship market

Austrian unemployment duration data

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

401(k) Pension Plan Participation and Net Financial Assets

Testing effect homogeneity across studies with double machine learning

ATET Estimation for Binary Treatments using Double Machine Learning

Sharp regression discontinuity design conditional on covariates

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

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

Difference-in-differences based on inverse probability weighting

identificationDML

Testing identification with double machine learning

Sales of video games

Difference-in-Differences for Mediation Analysis with Panel Data and Discrete Treatments Using Double Machine Learning

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

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

didcontDMLpanel

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

Dynamic treatment effect evaluation with double machine learning

Difference-in-Differences for Mediation Analysis with Repeated Cross-Sections and Discrete Treatments Using Double Machine Learning

Detection of instruments and control variables and validity testing with double machine learning