The Importance of Causality in Machine Learning

The traditional machine learning models that are kind of sitting on the shelves for us to use tend to make this assumption, this IID assumption that the data that it's trained on are all independently and identically distributed. And in many use cases, the decisions that we're making based on the models that we're using actually changes the distribution and the independence of the data in future time steps. That is one reason why when we're worried about algorithmic fairness and ethical AI, clearly causal inference has a lot to say about that. The example that I just gave, of course, is just making sure that our models, if we're making decisions that affect the training data that goes into our models