Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity

TL;DR

This paper introduces KEEL, a weakly-supervised causal discovery method that leverages fuzzy knowledge and data complementarity to improve accuracy and robustness in complex, high-dimensional, and small-sample datasets.

Contribution

The paper proposes KEEL, a novel fuzzy knowledge schema and integration of ELCM, enhancing causal discovery's robustness and accuracy with limited and error-prone prior knowledge.

Findings

KEEL outperforms state-of-the-art methods in accuracy and robustness.

KEEL demonstrates superior performance in protein signal transduction data.

The method improves causal discovery in high-dimensional, small-sample scenarios.

Abstract

Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. This schema not only lessens the dependency on expertise but also allows various types of limited and error-prone fuzzy knowledge to guide causal discovery. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios. In addition, we integrate the extended linear causal model (ELCM) into KEEL for dealing with the multi-distribution and incomplete data. Extensive experiments with different datasets demonstrate the superiority of KEEL over several state-of-the-art methods in accuracy, robustness and computational efficiency. For causal discovery in real protein signal transduction processes, KEEL outperforms the benchmark method with limited data. In summary, KEEL is effective to tackle the causal discovery tasks with higher accuracy while alleviating the requirement for extensive domain expertise.