Causal inference aims at the fundamental question of how changing the level of a cause or treatment can affect a subsequent outcome. Whether data analysts want to admit it or not, many analyses in behavioral, social, biomedical, and other fields of science are aimed at understanding causal relationships, even when the data or methods are not well suited to the task.nnThis presentation gives an overview of the causaldrf R package which addresses the relatively under-explored problem of estimating causal effects when the treatment is real-valued and continuous.
