Reviews: Causal Confusion in Imitation Learning

Summary: This paper has a very interesting claim: distributional shift in imitation learning settings is primarily caused by causal misidentification of the features by the learning algorithm. An interesting example is that of a self-driving car policy trained on a dataset of paired image-control datapoints collected by an expert human driving the car. If the images contain the turn signal on the dashboard then the supervised learner learns to have very good predictive power by indexing on that feature in the image. Clearly that does not generalize during test time. While this is a pathological example, such behavior is present in most settings where usually the state is blown-up by appending past states and actions.