Causal Discovery Using Proxy Variables

Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz

Abstract: In this paper, we develop a framework to estimate the cause-effect relation between two static entities x and y: for instance, an art masterpiece x and its fraudulent copy y. To this end, we introduce the notion of proxy variables, which allow the construction of a pair of random entities (A,B) from the pair of static entities (x,y). Then, estimating the cause-effect relation between A and B using an observational causal discovery algorithm leads to an estimation of the cause-effect relation between x and y. We evaluate our framework in vision and language.

TL;DR: We develop a framework for causal discovery between static entities such as an art masterpiece and its fraudulent copy.

Keywords: Causal Discovery, Vision, NLP