https://github.com/computorg/published-202509-boulet-simulator

Using neural networks to build more realistic simulation schemes for causal analysis
https://github.com/computorg/published-202509-boulet-simulator

counterfactual simulations variational-auto-encoders

Last synced: about 10 hours ago
JSON representation

Using neural networks to build more realistic simulation schemes for causal analysis

• Host: GitHub
• URL: https://github.com/computorg/published-202509-boulet-simulator
• Owner: computorg
• Created: 2025-03-17T08:48:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2026-07-06T17:46:46.000Z (2 days ago)
• Last Synced: 2026-07-06T18:06:05.992Z (2 days ago)
• Topics: counterfactual, simulations, variational-auto-encoders
• Language: TeX
• Homepage: https://computo-journal.org/published-202509-boulet-simulator/
• Size: 1.22 MB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Draw Me a Simulator

Sandrine Boulet, Antoine Chambaz

2025-09-08

### Citation

Sandrine Boulet and Antoine Chambaz (September 2025). Draw Me a Simulator. Computo.

### Badges

[![build and

publish](https://github.com/computorg/published-202509-boulet-simulator/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202509-boulet-simulator/actions/workflows/build.yml)

[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202509-boulet-simulator/issues?q=is%3Aopen+is%3Aissue+label%3Areview)

[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202509-boulet-simulator)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202509-boulet-simulator)

[![DOI:10.57750/w1hj-dw22](https://img.shields.io/badge/DOI-10.57750%2Fw1hj--dw22-034E79.svg)](https://doi.org/10.57750/w1hj-dw22)

[![Creative Commons

License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)

### Authors’ affiliations

- [Sandrine Boulet](https://bouletsandrine.wixsite.com/website) (Inria, Université Paris Cité, Inserm, HeKA, F-75015 Paris, France)

- [Antoine Chambaz](https://helios2.mi.parisdescartes.fr/~chambaz/) (Université Paris Cité, CNRS, MAP5, F-75006, Paris, France)

### Abstract

This study investigates the use of Variational Auto-Encoders to build a

simulator that approximates the law of genuine observations. Using both

simulated and real data in scenarios involving counterfactuality, we

discuss the general task of evaluating a simulator’s quality, with a

focus on comparisons of statistical properties and predictive

performance. While the simulator built from simulated data shows minor

discrepancies, the results with real data reveal more substantial

challenges. Beyond the technical analysis, we reflect on the broader

implications of simulator design, and consider its role in modeling

reality.