Deep Structural Causal

Deep structural causal models (DSCMs) combine deep learning with causal inference to analyze complex relationships in data and answer counterfactual questions—what would have happened if a specific intervention had occurred. Current research focuses on improving the accuracy and interpretability of DSCMs, often employing generative models like neural networks and diffusion models to estimate high-fidelity counterfactuals, particularly for high-dimensional data such as images. This field is significant because it allows researchers to move beyond simple correlations to understand causal mechanisms, impacting diverse fields from medical imaging and audio processing to more general causal reasoning in AI systems.