Delphina的动态

Transcript

Classically, machine learning is focused on prediction, predicting, and settings that look like this. What should you expect to see? What do you think is going to happen? Classical machine learning has many successes, but there is some sense in which it's not the most exciting. Because prediction is just about describing the world as it is in the status quo. And I don't know many people I talk with around here, they're not just interested in describing the world in the status quo, right? They want to build a company. They want to come up with a new medical treatment. They want people, in a literal sense, change the world in some kind of way. And if that's what you're interested in, then prediction is not the right task to understand. The concrete example, let's imagine your Airbnb Airbnb and 2008 and you try running a prediction algorithm, classical machine learning algorithm to see who would be interested in staying with an Airbnb host. The answer is going to be no one, right, because the company doesn't exist at the time. So it prediction is just somehow not the right task to for making progress in these settings. Causal and mail. Once you bring in causal inference, now you're in a world where you're answering more interesting questions. If I change my policy, how is this going to affect outcomes? What should I do to maximize, I don't know, revenue, right? These are questions that I think are often much more interesting. They don't fall under the scope of just prediction.