UPREVE: An End-to-End Causal Discovery Benchmarking System
Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, and K. Selcuk, Candan

TL;DR
UPREVE is a web-based system that simplifies causal discovery by providing an accessible GUI for running multiple algorithms, visualizing causal graphs, and evaluating their accuracy, thereby aiding researchers and practitioners.
Contribution
It introduces a comprehensive, user-friendly benchmarking platform for causal discovery that integrates multiple algorithms and visualization tools in a single interface.
Findings
Facilitates comparison of causal discovery algorithms.
Enhances understanding of causal relationships through visualization.
Improves accessibility for non-expert users.
Abstract
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evaluate (UPREVE), a user-friendly web-based graphical user interface (GUI) designed to simplify the process of causal discovery. UPREVE allows users to run multiple algorithms simultaneously, visualize causal relationships, and evaluate the accuracy of learned causal graphs. With its accessible interface and customizable features, UPREVE empowers researchers and practitioners in social computing and behavioral-cultural modeling (among others) to explore and understand causal relationships effectively. Our proposed solution aims to make causal discovery more accessible and user-friendly, enabling users to gain valuable insights for better decision-making.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Cognitive Science and Mapping
