External Course

The starting point of theories of probabilistic causation is that causes raise the probabilities of their effects; however the road to a satisfactory account of the relationship between causes and probabilisties has proven to be long and fraught with dificulties. This work offers a survey of the main problems and solutions emerged in about thirty years of research, and points to some metaphysical problems that are still object of philosophical debate.

CAUSAL INFERENCE IN STATISTICS - A PRIMER

On 19 Feb, 2021 By admin 0 Comments

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

Causal Inference: What If

On 19 Feb, 2021 By admin 0 Comments

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).

You are here

Pages