Telling cause from effect from purely observational data (focusing on pairs (X,Y) of variables rather than multi-variate case) has been a challenge at the NIPS 2008 workshop on Causality.  The scenario can be described as follows: 
 - Given observations $( x_1 , y_1 ), …, ( x_k , y_k )$ iid (independent and identically distributed) drawn from some distribution $P_{X , Y}$ , infer whether X causes Y (a) or Y causes X (b) , given the promise that exactly one of these alternatives is true. 

The NIPS 2013 challenge extended this setting to three classes:
$X\rightarrow Y (a)$ , $Y\rightarrow X (b)$, or the null class $\{(c),(d)\}$, which consisted of the unconnected case $X Y$ or the case of pure confounding $X \leftrightarrow Y$ (this notation is used as shorthand for X ← H → Y , where H is unobserved, but where neither X or Y cause each other). 

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

This contains the reference information for the books about causal inference. List of helpful books about causal inference:
1. The Book of Why
2. Causal inference in statistics : A primer
3. Explanation in Causal Inference: Methods for Mediation and Interaction 1st Edition (2015).  Author: VanderWeele, Tyler

Causal Inference Books

- 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

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

Causal inference in statistics : A primer

Cause Effect Pairs in Machine Learning

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/ 

Learn Before

Related

Learn After