M2DICR, or "Multimodal Deep Interpretable Causal Representation," is a framework designed to understand causal relationships in data by integrating multiple modalities. It leverages deep learning techniques to extract meaningful features while maintaining interpretability, allowing researchers to analyze how different factors influence outcomes. This approach is particularly useful in fields where understanding causation is crucial, such as healthcare and Social Sciences.