UPREVE: An End-to-End Causal Discovery Benchmarking System

Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, K. Selcuk Candan
7/25/2023
cs.LGcs.HCstat.ME

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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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Cite this paper

@article{unni2023upreve,
  title  = {UPREVE: An End-to-End Causal Discovery Benchmarking System},
  author = {Suraj Jyothi Unni and Paras Sheth and Kaize Ding and Huan Liu and K. Selcuk Candan},
  year   = {2023},
  eprint = {2307.13757},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.13757v1}
}

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