Some reflections on the use of structural equation modeling for investigating the causal relationships that affect search engine results

Search engines and recommender systems pervade everyday life and continuously make decisions regarding what information should be retrieved and how it should be ranked in order to meet the user’s information needs on the user’s behalf. Unfortunately, bias affects automated decision systems and as a consequence fairness cannot be taken for granted. Understanding whether and how bias affects search results can be a necessary and useful condition to every user and designer who aims to investigate the reasons that the systems fail or succeed. In this paper, we discuss whether Structural Equation Modeling (SEM) can be a useful methodology to investigate the causal relationships between the variables describing the content representation and retrieval processes of search engines and recommender systems. Understanding how and why a retrieval system retrieves certain documents can help understand when the system provides biased results. To this end, we provide a general illustration of the issues and the potential of SEM for causal discovery in Information Retrieval.