Causality

DSA ADS Course - 2021

Suffcient Causes: On Oxygen, Matches, and Fires

Probability, Causality, Causal Inference, Counterfactuals, Suffcient Causes, Explanation, Abduction

2019

Abstract

We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufcient cause of another, and how counterfactuals emerge organically from basic scientifc knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.

DSA ADS Course - 2021

Algorithms for Causal Reasoning in Probability Trees

Causality, Algorithms, Causal Reasoning, Probability Trees, Machine Learning

October, 2020

Abstract

2020

DSA ADS Course - 2021

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

February, 2019

DSA ADS Course - 2021

Abstract

February, 2019

Abstract

DSA ADS Course - 2021

Machine Learning, Causality, Causal Models, Knowledge Representation

May, 2021

Radical Empiricism and Machine Learning Research - Judea Pearl

Abstract

DSA External Course - 2021

April 8, 2021

Abstract

May, 2021

Abstract

December, 2019

Abstract

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning.
This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.