causalweight

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.

Last 30 days

This package has been downloaded 1,220 times in the last 30 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 47 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 8,968 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Mar 28, 2025 with 131 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.