metadata

title: Multiscaler
emoji: 🛰️
colorFrom: blue
colorTo: yellow
sdk: docker
pinned: false
license: mit
short_description: Illustrating "Optimizing Multi-Scale Representations"

Overview

This Shiny application allows users to visualize and explore results from the multi-scale representation approach described in the paper:

Fucheng Warren Zhu, Connor T. Jerzak, Adel Daoud. Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Application to Two Anti-Poverty RCTs. Forthcoming in Proceedings of the Fourth Conference on Causal Learning and Reasoning (CLeaR), 2025.

The app focuses on how varying the scale of Earth Observation (EO) inputs can affect conditional average treatment effect (CATE) estimation. It provides both a 2D heatmap and a 3D surface plot, helping researchers analyze how model performance metrics change with different multi-scale representations.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Reference

For more information, see arxiv.org/abs/2411.02134

@article{zhu2024encoding,
  title={Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs},
  author={Fucheng Warren Zhu and Connor T. Jerzak and Adel Daoud},
  journal={Forthcoming in Proceedings of the Fourth Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR)},
  year={2025},
  volume={},
  pages={},
  publisher={}
}