The Importance of Causality in Decision Making: A Perspective on Recommender Systems

TL;DR

This paper emphasizes the significance of causality in recommendation systems, proposing formal causal frameworks to improve decision-making and address biases in real-world applications.

Contribution

It introduces a formal causal modeling approach for recommendation systems using potential outcomes and causal graphs, guiding future research.

Findings

Formal definitions of causal quantities in RS

A general causal graph for RS analysis

Highlighting biases in recommendation algorithms

Abstract

Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS literature has repeatedly highlighted that, in real-world scenarios, recommendation algorithms suffer many types of biases since assumptions ensuring unbiasedness are likely not met. In this discussion paper, we formulate the RS problem in terms of causality, using potential outcomes and structural causal models, by giving formal definitions of the causal quantities to be estimated and a general causal graph to serve as a reference to foster future research and development.