External Course

March, 2019

Abstract

March, 2019

Abstract

February, 2018

Abstract

2013

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.

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).

February, 2021

Introduction

DSA ADS Course, 2021

Discuss various causal inference methods.

A Survey on Causal Inference - February, 2020

Abstract 

Code to accompany paper below: See: https://github.com/csblab/covid19-public/

Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line Michael Levitt, Andrea Scaiewicz, Francesco Zonta

See: https://www.datascienceassn.org/content/predicting-trajectory-any-covid19-epidemic-best-straight-line

June, 2020

Abstract