Using Causal ML Instead of A/B Testing

These problems are usually addressed through A/B testing. However, A/B tests have several requirements, among which not running too many tests at the same time. But real organizations are extremely messy, with different processes going on continuously. So, it is often impossible to assume that, while you run a test, everything else is being held constant. This is why, in this article, I will go through a different tool that allows addressing counterfactual questions, i.e. Causal Machine Learning. The benefits of Causal ML are that it is much more flexible and, most importantly, it can be used when you have little or no control on all the business processes (as is often the case for data scientists). <a href="https://towardsdatascience.com/using-causal-ml-instead-of-a-b-testing-eeb1067d7fc0">Learn More</a>