Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery

Mateusz Olko, Mateusz Gajewski, Joanna Wojciechowska, Łukasz Kuciński, Mikołaj Morzy, Piotr Sankowski, Piotr Miłoś

Keywords: causal discovery, faithfulness assumption, neural networks

TL;DR: We show that the faithfulness assumption limits the structure discovery accuracy of neural causal discovery method on standard benchmarks.

Abstract:

Neural-based causal discovery methods have recently improved in terms of scalability and computational efficiency. However, there are still opportunities for improving their accuracy in uncovering causal structures. We argue that the key obstacle in unlocking this potential is the faithfulness assumption, commonly used by contemporary neural approaches. We show that this assumption, which is often not satisfied in real-world or synthetic datasets, limits the effectiveness of existing methods. We evaluate the impact of faithfulness violations both qualitatively and quantitatively and provide a unified evaluation framework to facilitate further research.

Submission Number: 21