Judea Pearl - Computer Science and Statistics, University of California, Los Angeles, USA
Madelyn Glymour - Philosophy, Carnegie Mellon University, Pittsburgh, USA
Nicholas P. Jewell - Biostatistics and Statistics, University of California, Berkeley, USA
Miquel Hernan and Jamie Robins
The book is divided in three parts of increasing difficulty:
Part I is about causal inference without models (i.e., nonparametric identification of causal effects),
Part II is about causal inference with models (i.e., estimation of causal effects with parametric models).
Part III is about causal inference from complex longitudinal data (i.e., estimation of causal effects of time-varying treatments).
DSA ADS Course, 2021
Discuss various causal inference methods.
A Survey on Causal Inference - February, 2020