Causality

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

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.

Causes of Effects and Effects of Causes

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.

Causality for Machine Learning

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.

The Curse of Free-Will and the Paradox of Inevitable Regret

2013

Abstract

The paradox described below aims to clarify the principles by which empirical data are harnessed to guide decision making. It is motivated by the practical question of whether empirical assessments of the effect of treatment on the treated (ETT) can be useful for either policy evaluation or personal decisions.

Pages