External Course
Introduction to Coding Quantum Algorithms: A Tutorial Series Using Qiskit
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Probabilistic Causation
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.
CAUSAL INFERENCE IN STATISTICS - A PRIMER
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
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).
PATTERNS, PREDICTIONS, AND ACTIONS A story about machine learning
February, 2021
Introduction
A Survey on Causal Inference
DSA ADS Course, 2021
Discuss various causal inference methods.
A Survey on Causal Inference - February, 2020
Abstract
Simon Kornblith / GoogleAI - SimCLR and Paper Haul!
Code for: Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line
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
June, 2020
Abstract