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Implements a modern, unified estimation strategy for common mediation estimands (natural effects, organic effects, interventional effects, and recanting twins) in combination with modified treatment policies as described in Liu, Williams, Rudolph, and Díaz (2024) doi:10.48550/arXiv.2408.14620. Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning.

Last 30 days

This package has been downloaded 265 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.

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Last 365 days

This package has been downloaded 2,130 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Dec 05, 2024 with 58 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.