UPREVE: An End-to-End Causal Discovery Benchmarking System
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Abstract (arXiv)
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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References · 4
- 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
- 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
- 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
- 2020Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphscited by 4
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