Causal Inference Methods

January, 2020

Abstract

2016

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

Preface

2020

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

Abstract 

November, 2020

Abstract

2015

Abstract