Timezone: »

In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder · Guy Horowitz · Yoav Wald · Roi Reichart · Nir Rosenfeld

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #409

Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content---making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for \emph{how users causally perceive the environment}. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.

Author Information

Amir Feder (Columbia University)
Amir Feder

Amir Feder is a Postdoctoral Research Scientist in the Data Science Institute, working with Professor David Blei on causal inference and natural language processing. His research seeks to develop methods that integrate causality into natural language processing, and use them to build linguistically-informed algorithms for predicting and understanding human behavior. Through the paradigm of causal machine learning, Amir aims to build bridges between machine learning and the social sciences. Before joining Columbia, Amir received his PhD from the Technion, where he was advised by Roi Reichart and worked closely with Uri Shalit. In a previous (academic) life, Amir was an economics, statistics and history student at Tel Aviv University, the Hebrew University of Jerusalem and Northwestern University. Amir was the organizer of the First Workshop on Causal Inference and NLP (CI+NLP) at EMNLP 2021.

Guy Horowitz (Technion - Israel Institute of Technology, Technion - Israel Institute of Technology)
Yoav Wald (Johns Hopkins University)
Roi Reichart (Technion, Israel Institute of Technology)
Nir Rosenfeld (Technion, Technion)

More from the Same Authors