Reader small image

You're reading from  Causal Inference and Discovery in Python

Product typeBook
Published inMay 2023
PublisherPackt
ISBN-139781804612989
Edition1st Edition
Concepts
Right arrow
Author (1)
Aleksander Molak
Aleksander Molak
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

Right arrow

…and more

In this short section, we’ll introduce and briefly discuss three assumptions: the modularity assumption, stable unit treatment value assumption (SUTVA), and the consistency assumption.

Modularity

Imagine that you’re standing on the rooftop of a tall building and you’re dropping two apples. Halfway down, there’s a net that catches one of the apples.

The net performs an intervention for one of the apples, yet the second apple remains unaffected.

That’s the essence of the modularity assumption, also known as the independent mechanisms assumption.

Speaking more formally, if we perform an intervention on a single variable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>X</mml:mi></mml:math>, the structural equation for this variable will be changed (for example, set to a constant), yet all other structural equations in our system of interest will remain untouched.

Modularity assumption is central to do-calculus as it’s at the core of the logic of interventions.

Let’s see...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Causal Inference and Discovery in Python
Published in: May 2023Publisher: PacktISBN-13: 9781804612989

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