Causal Invariance and Machine Learning

#artificialintelligence 

One of the problems with these algorithms and the features they leverage is that they are based on correlational relationships that may not be causal. As Russ states: "Because there could be a correlation that's not causal. And I think that's the distinction that machine learning is unable to make--even though "it fit the data really well," it's really good for predicting what happened in the past, it may not be good for predicting what happens in the future because those correlations may not be sustained." This echoes a theme in a recent blog post by Paul Hunermund: "All of the cutting-edge machine learning tools--you know, the ones you've heard about, like neural nets, random forests, support vector machines, and so on--remain purely correlational, and can therefore not discern whether the rooster's crow causes the sunrise, or the other way round" I've made similar analogies before myself and still think this makes a lot of sense. However, a talk at the International Conference on Learning Representations definitely made me stop and think about the kind of progress that has been made in the last decade and the direction research is headed.