Books, and papers

Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python

The original article can be found here at Towards Data Science Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python: A Bayesian Method for Time Series Data Analysis and Forecasting in Python – by Yuefeng...

Efficient Intervention Design for Causal Discovery with Latents

Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco Professor Judea Pearl wrote:”An interesting paper on causal discovery using both observations and interventions. The problem is to recover a causal graph while...

Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects

Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf Professor Judea Pearl wrote: “This paper is the first COVID-19 analysis I read that goes beyond data-fitting and tells society: Yes! AI can be trusted to extract meaning from data, and can do...

A Guided Tour of Artificial Intelligence Research

Volume I: Knowledge Representation, Reasoning and Learning Introduction The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues....

On The Reasons Behind Decisions

Adnan Darwiche, Auguste Hirth Professor Judea Pearl commented “Speaking about “explainable AI”, this paper shows that, even in classification tasks, and even after agreeing on a Bayesian Network classifier, answering “why”...

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”...

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...