Causal discovery for image datasets via feature extraction

Artificial reasoning systems via Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress within the past decade. AI/ML systems have been able to reach unprecedented new levels of autonomy for a multitude of applications ranging from autonomous vehicles to biomedical imaging. This new level of intelligence and freedom for AI/ML systems requires them to have a degree of human-like intelligence in terms of causation beyond the correlation. This, however, has remained a major challenge for investigators when combining causality with AI/ML systems. AI/ML systems that are capable of generating cause and effect relationships are still in their infancy, as the literature highlights. The lack of investigations for causal reasoning systems that are capable of using datasets other than tabular data is well highlighted within literature. Causal learning for image, audio, video, radio-frequency, and other modalities still remain a major challenge. While there are open-source tools available for causal learning with tabular data, there is a lack of tools for other modalities. To this extent, this study proposes a causal learning method with image datasets by using existing tools and methodologies. Specifically, we propose to use existing causal discovery toolboxes for investigating causal relations within image datasets by converting image datasets into tabular form with feature extraction using tools such as auto-encoders and deep neural networks. The converted dataset can then be used to generate causal graphs by using tools such as the Causal Discovery Toolbox to highlight the specific cause and effect relations within the data. For AI/ML systems using causal learning for image datasets via existing tools and methodologies can provide an extra layer of robustness to ensure fairness and trustworthiness.