UCLA’s Professor Judea Pearl Wins the Rumelhart Prize

Recognition awarded for his leading research in artificial intelligence and reasoning systems. By RBR Staff | August 16, 2010 The Cognitive Science Society today announces that Professor Judea Pearl of University of California at Los Angeles...

Judea Pearl Tribute Symposium: Causality

UCLA computer science professor Judea Pearl was honored March 12, 2010 at an all-day workshop celebrating his influential contributions in artificial intelligence and related sciences. The event, held at the UCLA Faculty Center, coincided with the 25th...

Judea Pearl Bio

B.S. degree in Electrical Engineering from the Technion, Haifa, Israel, in 1960; Master degree in Physics from Rutgers University, New Brunswick, New Jersey, in 1965; Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn,...

JUDEA PEARL Lecture Video

References https://amturing.acm.org/vp/pearl_2658896.cfm

An AI Pioneer Wants His Algorithms to Understand the ‘Why’

Deep learning is good at finding patterns in reams of data, but can’t explain how they’re connected. Turing Award winner Yoshua Bengio wants to change that. “It’s a big thing to integrate [causality] into AI,” says University...

Introducing DoWhy

The human mind has a remarkable ability to associate causes with a specific event. From the outcome of an election to an object dropping on the floor, we are constantly associating chains of events that cause a specific effect. Neuropsychology...

Using Causal Reasoning To Guide Algorithms Toward a Fairer World

ILYA SHPITSER John C. Malone Assistant Professor of Computer Science, Johns Hopkins University DANIEL MALINSKY Researcher, Johns Hopkins University   Learning algorithms, which are becoming an increasingly ubiquitous part of our lives, do precisely...

From how to why: An overview of causal inference in machine learning

Ravi Pandya Artificial intelligence is good at predicting outcomes, but how do we go one step further? Here, we discuss how AI can use causal inference and machine learning to measure the effects of multiple variables – and why it’s important for...

EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation

EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal to combine state-of-the-art...