Machine learning for causal inference in Biostatistics

General inference problems and quantifying uncertainty have long been the cornerstone of statistical science. While machine learning advances have permeated many disciplines, inference for these procedures, and in particular, causal inference, has not been widespread. However, this is rapidly changing. As different scientific fields begin to converge on machine learning for causal inference, we thought now would be an excellent time to have a public discussion. In our roles as editors of Biostatistics, we decided to organize a series of commentaries on the topic from scholars with expertise in statistics, computer science, epidemiology, health economics, policy, and law.