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Causal Discovery Using Proxy Variables
Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
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
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