Probabilistic Causation

DSA ADS Course - 2022

Graphical Models, Bayesian Networks, Applied Probability, Probabilistic Causation, Causal Inference

Applied data scientists require high-level understanding of probability theory and specialized techniques to design systems and solve problems in the real world.  Graphical model formalism provides a natural framework for the design of new data science systems.

DSA ADS Course - 2022

Probability Theory, Applied Probability, Probabilistic Causation, Causal Inference

Understanding applied probability and accurate assessment of real-world risk is a critical part of high value data science.

January, 2020

Abstract

June, 2020

DSA ADS Course - 2021

Forecasting, COVID19, John Ioannidis, Public Policy, Health Policy, Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation

Forecasting is usually impossible in high causal density environments. Scenario planning with applied probability and adaptation to near real-time data is optimal strategy. Epidemic forecasting is usually a fools errand yet appropriate analysis of experience and historical precedent is helpful.

March, 2020

DSA ADS Course - 2021

Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation

Causality - intuitive causation vs. probabilistic causation. Probabilistic causation is optimal yet discuss other techniques to seek true causation. Sometimes true causation is impossible to find and discuss optimal strategy under such circumstances.

Abstract

2014

Abstract

This paper summarizes methods that were found useful in estimating the probability that one event was a necessary cause of another, as interpreted by law makers. We show that the fusion of observational and experimental data can yield informative bounds which, under certain circumstances, meet legal criteria of causation. We further investigate the circumstances under which such bounds can emerge, and the philosophical dilemma associated with determining individual cases from statistical data.

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.