I have recently developed a keen interest in the Causal inference literature stemming from the Computer Science community (Judea Pearl) and other disciplines such as Philosophy of Science (Clark Glymour, Peter Spirtes, James Woodward) and Physics/Mathematics (Dominik Janzing, Bernhard Schölkopf). It has become popular in some research areas (e.g. machine learning, biomedicine), but is yet to bear fruits among applied economists.
I am teaching to Master students in Data Science some notions of causal graphs (d-separation, backdoor criterion, do-operator):
Special credits to: Scott Cunningham (The Mixtape), Matheus Facure Alves (Causal Inference for the Brave and True), Nick Huntington-Klein (The Effect), Juan Camilo Orduz, and the whole team contributing to the DoWhy Python library