Estimation Methods for Causal Inference Based on Inverse Probability Weighting
Instrument-based treatment evaluation under endogeneity and non-respon...
Local average treatment effect estimation based on inverse probability...
Causal mediation analysis with double machine learning
Causal mediation analysis with instruments for treatment and mediator ...
Causal mediation analysis based on inverse probability weighting with ...
Causal mediation analysis with a continuous treatment based on weighti...
paneltestDML: Overidentification test for ATET estimation in panel dat...
Sharp regression discontinuity design conditional on covariates
Test for identification in causal mediation and dynamic treatment mode...
Binary or multiple discrete treatment effect evaluation with double ma...
Binary or multiple treatment effect evaluation with double machine lea...
Treatment evaluation based on inverse probability weighting with optio...
Local average treatment effect estimation in multiple follow-up period...
Continuous Difference-in-Differences using Double Machine Learning for...
Continuous Difference-in-Differences using Double Machine Learning for...
Difference-in-Differences in Repeated Cross-Sections for Binary Treatm...
Difference-in-differences based on inverse probability weighting
Dynamic treatment effect evaluation with double machine learning
Testing identification with double machine learning
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) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.