Using causality theory to correct for training set bias - Onno Zoeter

The classic supervised learning problem that is taught in machine learning courses and is the subject of many machine learning competitions, is often too narrow to reflect the problems that we face in practice. Historical datasets typically reflect a combination of a source of randomness (for example customers making browsing and buying decisions) and a controlling mechanism such as a ranker or highlighting heuristics (badges, promotions, etc.). Or there might be a selection mechanism (such as the decision to not accept transactions with high fraud risk) that influences the training data. A straightforward regression approach would not be able to disentangle the influence of the controller and the phenomenon under study. As a result it risks making incorrect predictions as the controller is changed.
In practice however, such problems are typically treated as a classic regression problem in a first iteration and attempts to identify and correct for these complications come as afterthoughts or are not undertaken at all. Ideally there is a rigorous and flexible formalism that captures the correct framing of the problem from the very start, accompanied by a set of practical algorithms that work well in practice for each of the identified cases.
This research objective is the main goal of the Mercury Machine Learning Lab, one of the labs in Booking AI Research. The Mercury lab is a collaboration between the University of Amsterdam, the Technical University of Delft and Booking.com. It brings together the fields of information retrieval, causality and reinforcement learning where the topic is studied under the names of off-line evaluation, transferability and s-recoverability and off-policy learning respectively.
This presentation will sketch the problem, highlights some of the theoretical results so far and describes a significant real-world application.