AIWS University

Causal Inference Without Counterfactuals: Comment

Judea Pearl 1. BACKGROUND The field of statistics has seen many well-meaning crusades against threats from metaphysics and other heresy. In its founding prospectus of 1834, the Royal Statistical Society resolved “to exclude carefully all Opinions...

Decision-theoretic foundations for statistical causality

A. Philip Dawid We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic statistical causality (DT), which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as...

Journal of Causal Inference

Your benefits Interdisciplinary approach Quantitative methodology Outstanding editorial board One of leading journals in causal inference Open access publication Objective Journal of Causal Inference (JCI) is a fully peer-reviewed, open...

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu Recent progress in deep learning is essentially based on a “big data for small tasks”...

Tweets by Judea Pearl

Tweets by yudapearl

Causal Inference that’s not A/B Testing: Theory & Practical Guide

WRITTEN BY Eva Gong Undoubtedly, randomized experimentation (assuming it is conducted properly) is the most straightforward way to establish causality (refer to my previous article on a collection of A/B testing learning resources!). However, practically...

Generalizing Experimental Results by Leveraging Knowledge of Mechanisms

Abstract We show how experimental results can be generalized across diverse populations by leveraging knowledge of mechanisms that produce the outcome of interest. We use Structural Causal Models (SCM) and a refined version of selection diagrams to...

Graphical Models for Processing Missing Data

This paper reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: \textit{transparency, estimability...

Full Law Identification In Graphical Models Of Missing Data: Completeness Results

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence...