Causality Based Perspective

Causality-based perspectives are transforming various machine learning fields by moving beyond simple correlations to understand and model cause-and-effect relationships. Current research focuses on leveraging causal inference techniques, such as backdoor adjustment and counterfactual reasoning, to address challenges like bias in data, improve generalization in models (e.g., for image restoration and emotion recognition), and enhance the trustworthiness and alignment of AI systems. This shift towards causal reasoning promises more robust, reliable, and interpretable AI models with significant implications for applications ranging from autonomous driving to controllable text generation.