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

arXiv: https://arxiv.org/abs/2307.13757 · HTML: https://mlsanity.com/paper/2307.13757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [gCastle: A Python Toolbox for Causal Discovery](https://mlsanity.com/paper/2111.15155/) (2021)
- [Causal Discovery Toolbox: Uncover causal relationships in Python](https://mlsanity.com/paper/1903.02278/) (2019)
- [Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs](https://mlsanity.com/paper/2010.04917/) (2020)

## 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.