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Causal AI at KDD 2024 — Why Companies That Won’t Jump on the Causal Train Now Will Have a Harder Time Competing in 2025 and Beyond
Building Causal Expertise is a Process, Not an Event
Causal modeling is an umbrella term for a wide range of methods that allow us to model the effects of our actions on the world.
Causal models differ from traditional machine learning models in a number of ways.
The most important distinction between them stems from the fact that the information contained in observational data used to train traditional machine learning machinery is — in general — insufficient to consistently model the effects of our actions.
The result?
Using traditional machine learning methods to model the outcomes of our actions leads — in principle — to biased decisions.
A good example here is using a regression model trained on historical data for marketing mix modeling.
Another one?
Using XGBoost trained on historical observations to predict the probability of churn and sending a campaign if the predicted probability of churn is greater than some threshold.
