causal-relations-between-representations

Overview

NCINet is an approach for observational causal discovery from high-dimensional data. It is trained purely on synthetically generated representations and can be applied to real representations. It’s also be applied to analyze the effect on the underlying causal relation between learned representations induced by various design choices in representation learning.

Dataset

How to evalute NCInet (generalization)

How to evalute NCInet (representations)

@inproceedings{
  wang2022ncinet,
  title={Do learned representations respect causal relationships?},
  author={Lan Wang and Vishnu Naresh Boddeti},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}