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Causal Inference and Discovery in Python

You're reading from   Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Length 456 pages
Edition 1st Edition
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Author (1):
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Aleksander Molak Aleksander Molak
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Aleksander Molak
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Causality – an Introduction
2. Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? FREE CHAPTER 3. Chapter 2: Judea Pearl and the Ladder of Causation 4. Chapter 3: Regression, Observations, and Interventions 5. Chapter 4: Graphical Models 6. Chapter 5: Forks, Chains, and Immoralities 7. Part 2: Causal Inference
8. Chapter 6: Nodes, Edges, and Statistical (In)dependence 9. Chapter 7: The Four-Step Process of Causal Inference 10. Chapter 8: Causal Models – Assumptions and Challenges 11. Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners 12. Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More 13. Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond 14. Part 3: Causal Discovery
15. Chapter 12: Can I Have a Causal Graph, Please? 16. Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications 17. Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond 18. Chapter 15: Epilogue 19. Index 20. Other Books You May Enjoy

A brief history of causality

Causality has a long history and has been addressed by most, if not all, advanced cultures that we know about. Aristotle – one of the most prolific philosophers of ancient Greece – claimed that understanding the causal structure of a process is a necessary ingredient of knowledge about this process. Moreover, he argued that being able to answer why-type questions is the essence of scientific explanation (Falcon, 2006; 2022). Aristotle distinguishes four types of causes (material, formal, efficient, and final), an idea that might capture some interesting aspects of reality as much as it might sound counterintuitive to a contemporary reader.

David Hume, a famous 18th-century Scottish philosopher, proposed a more unified framework for cause-effect relationships. Hume starts with an observation that we never observe cause-effect relationships in the world. The only thing we experience is that some events are conjoined:

We only find, that the one does actually, in fact, follow the other. The impulse of one billiard-ball is attended with motion in the second. This is the whole that appears to the outward senses. The mind feels no sentiment or inward impression from this succession of objects: consequently, there is not, in any single, particular instance of cause and effect, any thing which can suggest the idea of power or necessary connexion” (original spelling; Hume & Millican, 2007; originally published in 1739).

One interpretation of Hume’s theory of causality (here simplified for clarity) is the following:

  • We only observe how the movement or appearance of object A precedes the movement or appearance of object B
  • If we experience such a succession a sufficient number of times, we’ll develop a feeling of expectation
  • This feeling of expectation is the essence of our concept of causality (it’s not about the world; it’s about a feeling we develop)

Hume’s theory of causality

The interpretation of Hume’s theory of causality that we give here is not the only one. First, Hume presented another definition of causality in his later work An Enquiry Concerning the Human Understanding (1758). Second, not all scholars would necessarily agree with our interpretation (for example, Archie (2005)).

This theory is very interesting from at least two points of view.

First, elements of this theory have a high resemblance to a very powerful idea in psychology called conditioning. Conditioning is a form of learning. There are multiple types of conditioning, but they all rely on a common foundation – namely, association (hence the name for this type of learning – associative learning). In any type of conditioning, we take some event or object (usually called stimulus) and associate it with some behavior or reaction. Associative learning works across species. You can find it in humans, apes, dogs, and cats, but also in much simpler organisms such as snails (Alexander, Audesirk & Audesirk, 1985).

Conditioning

If you want to learn more about different types of conditioning, check this https://bit.ly/MoreOnConditioning or search for phrases such as classical conditioning versus operant conditioning and names such as Ivan Pavlov and Burrhus Skinner, respectively.

Second, most classic machine learning algorithms also work on the basis of association. When we’re training a neural network in a supervised fashion, we’re trying to find a function that maps input to the output. To do it efficiently, we need to figure out which elements of the input are useful for predicting the output. And, in most cases, association is just good enough for this purpose.

You have been reading a chapter from
Causal Inference and Discovery in Python
Published in: May 2023
Publisher: Packt
ISBN-13: 9781804612989
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