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Published inMay 2023
PublisherPackt
ISBN-139781804612989
Edition1st Edition
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Aleksander Molak
Aleksander Molak
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Aleksander Molak

Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.
Read more about Aleksander Molak

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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.

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Author (1)

author image
Aleksander Molak

Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.
Read more about Aleksander Molak