Causal inference

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).

I am teaching to Master students in Data Science some notions of causal graphs (d-separation, backdoor criterion, do-operator) - which I am quite confident that is a couple of years from now will become more popular among applied economists and managers:

1. introduction

2. causal graphs 101

3. Pearl's do-operator

4. "Beware of the DAG"

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