Visual Causality Discovery

Visual causality discovery aims to understand and model cause-and-effect relationships within visual data, improving the interpretability and reliability of machine learning models. Current research focuses on developing methods to identify causal influences within complex systems, such as autonomous driving and medical image analysis, often employing neural networks with modules designed to extract and leverage causal signals, including attention mechanisms and Markov blankets. This work is crucial for enhancing the trustworthiness of AI systems and enabling more reliable and insightful analyses across diverse scientific and engineering domains.