Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph

Abstract

In causal learning, discovering the causal graph of the underlying generative mechanism from observed data is crucial. However, real-world data for causal discovery is scarce and expensive, leading researchers to rely on synthetic datasets, which may not accurately reflect real-world performance. To address this, we propose a novel method for evaluating causal discovery algorithms without needing real causal graphs. Specifically, our method employs deep learning evaluation strategies and ensemble learning techniques to robustly assess the performance of causal discovery methods. To elaborate, our approach emulates deep learning validation strategies by dividing the data into training and testing sets. We perform causal discovery on the training set and subsequently use the testing set to conduct Markov blanket tests on the node set and causal direction determination on the edge set. Moreover, we employ multiple ensemble strategies to ensure a comprehensive evaluation of the algorithms. Furthermore, experiments on both synthetic and real datasets demonstrate our method’s effectiveness in accurately and comprehensively validating causal discovery algorithms. Our results show that our proposed method can reflect the performance of causal discovery methods in practice with reasonable error.

Keywords

• Causal discovery
• Markov blanket
• causal graphical models
• cause effect identification
• condition independence testing