Book Image

Causal Inference and Discovery in Python

By : Aleksander Molak
4.7 (9)
Book Image

Causal Inference and Discovery in Python

4.7 (9)
By: Aleksander Molak

Overview of this book

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.
Table of Contents (21 chapters)
Part 1: Causality – an Introduction
Part 2: Causal Inference
Part 3: Causal Discovery


Alexander, J. E., Audesirk, T. E., & Audesirk, G. J. (1985). Classical Conditioning in the Pond Snail Lymnaea stagnalis. The American Biology Teacher, 47(5), 295–298.

Archie, L. (2005). Hume’s Considered View on Causality. [Preprint] Retrieved from: (accessed 2022-04-23)

Falcon, A. “Aristotle on Causality”, The Stanford Encyclopedia of Philosophy (Spring 2022 Edition), Edward N. Zalta (ed.). Retrieved 2022-04-23

Gopnik, A. (2009). The philosophical baby: What children’s minds tell us about truth, love, and the meaning of life. New York: Farrar, Straus and Giroux

Gutierrez, P., & Gérardy, J. (2017). Causal Inference and Uplift Modelling: A Review of the Literature. Proceedings of The 3rd International Conference on Predictive Applications and APIs in Proceedings of Machine Learning Research, 67, 1-13

Hernán M. A., & Robins J. M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC

Hume, D., & Millican, P. F. (2007). An enquiry concerning human understanding. Oxford: Oxford University Press

Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux

Lorkowski, C. M. Retrieved 2022-04-23

Stahl, A. E., & Feigenson, L. (2015). Cognitive development. Observing the unexpected enhances infants’ learning and exploration. Science, 348(6230), 91–94.