Causal Future Prediction in a Minkowski Space-Time

Athanasios Vlontzos, Henrique Bergallo Rocha, Daniel Rueckert, Bernhard Kainz

Keywords: Causal Discovery, Future Prediction

Abstract: Estimating future events is a difficult task. Unlike humans, machine learning ap- proaches are not regularized by a natural understanding of physics. In the wild, a plausible succession of events is governed by the rules of causality, which can- not easily be derived from a finite training set. In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatio- temporal information on a Minkowski space-time. We utilize the concept of a light cone from special relativity to restrict and traverse the latent space of an arbitrary model. We demonstrate successful applications in causal image synthe- sis and future video frame prediction on a dataset of images. Our framework is architecture- and task-independent and comes with strong theoretical guarantees of causal capabilities.

One-sentence Summary: We provide a theoretically guaranteed method for causal future prediction

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