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.
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
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
Machine learning is a subfield of computer science and data science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.
Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.