Telling cause from effect from purely observational data, focusing on pairs of variables $$(X, Y)$$, was introduced as a challenge at the NIPS 2008 workshop on causality. The basic scenario is: given independent and identically distributed (i.i.d.) observations $$(x_1, y_1), \dots, (x_k, y_k)$$ drawn from a distribution $$P_{X,Y}$$, infer whether $$X$$ causes $$Y$$ or $$Y$$ causes $$X$$, given the promise that exactly one is true. The NIPS 2013 challenge extended this to three classes: $$X \rightarrow Y$$, $$Y \rightarrow X$$, or a null class consisting of either the independent case $$X \perp Y$$ or the pure confounding case $$X \leftrightarrow Y$$ (shorthand for $$X \leftarrow H \rightarrow Y$$, where $$H$$ is an unobserved confounder).

Cause-Effect Pair Challenge

 Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu (2019)
Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
https://www.amazon.com/Effect-Machine-Learning-Springer-Challenges/dp/3030218090

University of Michigan - Ann Arbor

- Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Machine Learning: A Probabilistic Perspective
- Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems by Brett Lantz

Machine Learning Books

This node categorizes references from published books in the field of causal inference.

Causal Inference Books

The Hundred-Page Machine Learning Book is, in reality, about 130 pages. However, it covers a broad range of topics from introductory math (primarily vector and matrix transformations) to building statistical models to the new field of deep learning. It's very dense in content, so it's not like reading a typical 130-page book, but can be finished within a week or two. It's not a textbook so it doesn't have exercises to give you hands on experience, but it's very helpful in glossing a vast scope of machine learning concepts. 

Reference: Burkov, A. (2019). The Hundred-page Machine Learning Book. Leipzig: Andriy Burkov.

PDF Link: [themlbook.com/wiki/doku.php](https://themlbook.com/wiki/doku.php) 

The Hundred-Page Machine Learning Book by Andriy Burkov

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective . MIT Press.

Machine Learning: A Probabilistic Perspective

Lantz, Brett (2015). Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems. Packt Publishing. 

Machine Learning with R (Second Edition)

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

Pattern Recognition and Machine Learning, Bishop

Pearl, J., & Mackenzie, D. (2018). $$\mathit{The \ Book \ of \ Why: \ The \ New \ Science \ of \ Cause \ and \ Effect.}$$ Basic Books.

The Book of Why

Explanation in Causal Inference: Methods for Mediation and Interaction 1st Edition (2015)
Author: VanderWeele, Tyler

Explanation in Causal Inference: Methods for Mediation and Interaction 1st Edition

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics : A primer. Retrieved from https://ebookcentral-proquest-com.proxy.lib.umich.edu

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