Books, and papers

Home » Practicing Principles » Modern Causal Inference » Augmenting » Books, and papers »

Books, and papers

A Guided Tour of Artificial Intelligence Research

14 May, 2020

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

13 May, 2020

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

06 May, 2020

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

28 Apr, 2020

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...

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

22 Apr, 2020

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...


18 Apr, 2020

Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard...