#YouAsked: "Why can't use GenAI tools to do causal analytics?"

I've had a lot of people ask honest questions about why they can't use GenAI tools to look at cause and effect. "It would be so much easier to just be able to do it in one tool!"

I get it.

The problem is that there are laws in math and science that you don't just wave a magic wand and get rid of. This idea is often not popular in professional cultures who prize the "hack," but this is like #Gravity. It honestly doesn't care if we don't agree with it, or if you want some other option. If you jump off a building to prove that you don't believe in Gravity, the outcome is still predetermined.

What is Causal AI?

Causal AI is the integration of predictive inference based on repeating patterns -- the next ____________ in the pattern that's predicted -- and causal inference, which explains the how's and why's of large network effects that include the patterns.

This is the definitive relationship between #Correlation and #Causation. Correlation is part of the discovery mechanism to ultimately create a causal model, but correlation is in no way the end result.

Another question I get a lot is very practical in its motivation: "ok, how far apart is a correlation based model from a causal model? Like, if I went with correlation, how far off from reality would I be? Maybe that's a variance I can live with?"

Fair question. Easy answer.

To give you a specific, I asked Claude and ChatGPT for help. Here's the summary of a much longer answer:

Quantifying the Gap: Simulations & Real-World Comparisons

In controlled simulations, correlation-based ML models can be 20-60% off in estimating true causal effects—sometimes more. The more volatility there is in the external variables, for example, the more likely the distortion is to be on the high side.

Expressed in navigation terms, ChatGPT expressed this sort of distortion in decision-making in navigational terms:

A 50% correlation error is the equivalent of setting a 90° incorrect course in navigation—guaranteeing you’ll completely miss your target unless corrected early.

In business forecasting, misattributing causation can lead to millions in wasted spend due to false assumptions about drivers of revenue, churn, or efficiency. The time lag between when you invest in these assumptions and when you find out you were wrong magnifies their impact by as much as 5-6X. This is the weight of opportunity cost. A million spent ineffectively based on correlation analysis grows to at least $5 million in total downside over 18 months.

This issue is REALLY IMPORTANT.

It's why I spend so much effort in educating the marketplace so that you know what's what.

The Multi-Touch Attribution movement began to collapse because it was correlation based, and correlation does not imply causality. More and more vendors, trying to hop on the causal modeling train, have been attempting to sell causation tools that work on correlation only. Even big tech has done this: #MetaRobyn, #GoogleMeridian, and #AdobeMMM are all correlation-based. They try to spruce it up with terms like #DoubleML, but the reality is that when you run the numbers, you are way off the mark in comparison to real Causal AI.

Why? Because they know that Marketing teams prioritize #easy.

But when Finance teams look at analytics products, they want #reality.

Big Change, Right Action, Real Proof.