The Yuan requests your support! Our content will now be available free of charge for all registered subscribers, consistent with our mission to make AI a human commons accessible to all. We are therefore requesting donations from our readers so we may continue bringing you insightful reportage of this awesome technology that is sweeping the world. Donate now
Causation: The most misunderstood concept in AI
By Scott Burk  |  Feb 21, 2023
Causation: The most misunderstood concept in AI
Image courtesy of and under license from
This article is part two of the ongoing series: AI Prediction, AI Prescription and Causation in Medicine. Just as correlation and causation are often conflated, so too are predictive and prescriptive models. Data science expert and It’s All Analytics founder Scott Burk sheds light on the key differences, which are most applicable to which situations, and how to avoid misapplications.

AUSTIN, TEXAS - Artificial intelligence (AI) applications in healthcare are improving clinical decision making and therapies and enhancing clinical and operational efficiencies. They offer the potential to radically transform medical practice and improve global health. While this is great news, the misapplication of AI models can easily lead to undesirable results: To avoid this, the formulation and application of AI models require different philosophical and mathematical assumptions.

This article focuses on revealing different model assumptions - specifically the difference between predictive and prescriptive models - which is a greatly misunderstood topic for AI in healthcare.

Correlation versus causation

In every Statistics 101 class, students learn that ‘correlation is not causation.’ With good reason, one of this author’s favorite quotes is “if you torture the data long enough, they will admit to anything.” In other words, if one compares enough pairs of variables, one will inevitably find some with high degrees of correlation. However, this is happenstance correlation, and it is the worst type of correlation when it comes to AI modeling. Why? The answer is that these variables simply have nothing in common from a logical standpoint. Some examples from author, military analyst, and Spurious Correlations website creator Tyler Vigen:

Correlation between US spending on science vs. suicides: 99.79 percent (yearly, 1999 to 2009)

Correlation between the number of people who drowned after falling out of a fishing boat vs. the marriage rate in Kentucky: 95.24 percent

One can call these ‘useless correlations’ for AI: such correlations do not provide any useful insights, nor is there any causative relationship.

Then there are other relationships where there is a rati

The content herein is subject to copyright by The Yuan. All rights reserved. The content of the services is owned or licensed to The Yuan. Such content from The Yuan may be shared and reprinted but must clearly identify The Yuan as its original source. Content from a third-party copyright holder identified in the copyright notice contained in such third party’s content appearing in The Yuan must likewise be clearly labeled as such.
Continue reading
Sign up now to read this story for free.
- or -
Continue with Linkedin Continue with Google
Share your thoughts.
The Yuan wants to hear your voice. We welcome your on-topic commentary, critique, and expertise. All comments are moderated for civility.