Learning causal inference models, frameworks, IPTW and causal ML approaches

This is not my code; these are tutorial examples in python and R that I have learned from in the process of understanding fundamental principles and methods around the approximation of causal effects in real-world data in preparation for creation of my thesis models.

The following people wrote and/or designed the code here: Fiedler - python g-methods code; Hernan & Robins book supplement Lucy D'Agostino McGowan & Malcolm Barrett - R causal inference tutorial using propensity score matching and DAGs Babette Brumback - Fundamentals of Causal Inference with R (book examples) Susan Athey & Stefan Wager (paper) and Golub Capital Social Impact Lab (authors Kaleb K. Javier, Niall Keleher, Sylvia Klosin, Nicolaj Søndergaard Mühlbach, Xinkun Nie, and Matt Schaelling) - Tutorial - ML based causal inference for average and heterogenous treatment effects