As a result of this joint learning, splits in the random forest are more likely to occur along informative genetic features that are orthogonal (that is, not correlated) to population structure.

A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of ...

ProPublica borrowed machine learning methods from academic research to better understand links between forest loss and spillover risk. The results were surprising, but led us to a story we wouldn ...