Evaluating Causal Discovery without Ground Truth

Core Concepts

Compatibility constraints can be used to falsify the output of causal discovery algorithms in the absence of ground truth.

Abstract

The article discusses a novel method for evaluating causal discovery algorithms without relying on causal ground truth. It introduces the concept of self-compatibility, where the outputs of an algorithm are tested for compatibility across different subsets of variables. By detecting incompatibilities, the method can falsify wrongly inferred causal relations due to violated assumptions or errors from finite sample effects. The article emphasizes that passing compatibility tests provides strong evidence for the accuracy of causal models. It also presents experimental results showing how detection of incompatibilities can aid in model selection and parameter tuning. Additionally, it proposes an incompatibility score based on interventional and graphical compatibility notions to quantify the level of incompatibility between outputs on different subsets of variables.

Stats

Proceedings: 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024, Valencia, Spain.
PMLR Volume: 238.
Copyright: 2024 by the author(s).

Quotes

"We propose a novel methodology for the falsification of the outputs of causal discovery algorithms on real data without access to causal ground truth."
"While statistical learning aims for stability across different subsets of data points, we argue that causal discovery should aim to achieve stability across different subsets of variables."
"Our results show a significant correlation between the score and SHD."

Key Insights Distilled From

Self-Compatibility

by Philipp M. F... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2307.09552.pdf

Deeper Inquiries

How can self-compatibility be utilized beyond falsifying causal discovery algorithms

Self-compatibility can be utilized beyond falsifying causal discovery algorithms by serving as a criterion for model selection and evaluation. Algorithms that exhibit high self-compatibility scores could potentially be considered more reliable in predicting statistical properties of unobserved joint distributions. This approach could help researchers identify robust causal models that generalize well across different variable sets, providing valuable insights into complex systems' behavior.

What are potential limitations or criticisms regarding using compatibility constraints for evaluating causal models

One potential limitation of using compatibility constraints for evaluating causal models is the assumption that stability across subsets of variables implies accurate predictions about system behavior under interventions. While self-compatibility can provide strong evidence for the reliability of causal models, it may not guarantee their accuracy in predicting causal effects or outcomes in real-world scenarios where interventions are involved. Additionally, the method's effectiveness may depend on the quality and quantity of data available, as well as the complexity of the underlying causal relationships.

How might this approach impact future developments in causal inference methodologies

This approach to utilizing compatibility constraints for evaluating causal models could have significant implications for future developments in causal inference methodologies. By focusing on self-compatibility as a measure of model reliability and generalization ability, researchers may shift towards more robust and stable algorithms that produce consistent results across different subsets of variables. This emphasis on compatibility could lead to advancements in understanding complex causality patterns and improving the overall performance of causal discovery methods in various fields such as healthcare, economics, social sciences, and more.