Structural Causal Model

Structural causal models (SCMs) are a framework for representing causal relationships between variables, aiming to move beyond mere correlation and understand cause-and-effect. Current research focuses on applying SCMs to diverse domains, including multimodal language models, healthcare, and recommendation systems, often employing techniques like backdoor adjustment and counterfactual reasoning to mitigate confounding biases and improve model interpretability. This work is significant because it enables more robust and reliable causal inference, leading to improved decision-making in various fields and a deeper understanding of complex systems. The development of new algorithms for causal discovery and parameter estimation within SCMs, including those leveraging neural networks and graph representations, is a key area of ongoing investigation.