Learn R Programming

causalweight (version 1.1.5)

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).

Copy Link

Version

Install

install.packages('causalweight')

Monthly Downloads

493

Version

1.1.5

License

MIT + file LICENSE

Maintainer

Hugo Bodory

Last Published

June 8th, 2026

Functions in causalweight (1.1.5)

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

India's National Health Insurance Program (RSBY)

Local average treatment effect estimation based on inverse probability weighting

identificationDML

Testing identification with double machine learning

Dynamic treatment effect evaluation with double machine learning

Causal mediation analysis with double machine learning

didcontDMLpanel

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

Sales of video games

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

Difference-in-differences based on inverse probability weighting

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

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

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

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

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

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

Wage expectations of students in Switzerland

Austrian unemployment duration data

Swedish municipalities

Correspondence test in Swiss apprenticeship market

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

Sharp regression discontinuity design conditional on covariates

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

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

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

ATET Estimation for Binary Treatments using 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.

Expenditure and Default Data