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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 1048385, 11 pages
https://doi.org/10.1155/2017/1048385
Research Article

An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

1Koc University, Istanbul, Turkey
2Bilkent University, Ankara, Turkey

Correspondence should be addressed to Ibrahim Delibalta; rt.ude.uk@31atlabiledi

Received 2 March 2017; Revised 20 April 2017; Accepted 3 May 2017; Published 22 June 2017

Academic Editor: Zhixin Yang

Copyright © 2017 Ibrahim Delibalta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.