Causal Structure Learning for Robust and Explainable Artificial Intelligence

Modern artificial intelligence (AI) has achieved success by learning statistical patterns from data and framing many problems as prediction tasks. However, prediction alone cannot answer questions such as why an outcome occurred or how an intervention would change it. Answering these questions requires reasoning about cause and effect. Causal AI addresses this limitation by incorporating causal inference into AI. This thesis advances that goal through causal structure learning, focusing on two interrelated challenges: causal forecasting and causal discovery. First, it demonstrates that causal feature selection can improve both the predictive performance and explainability of AI forecasting. It then extends this idea to non-stationary environments by modeling regime-dependent causal structures, providing improved forecasts and interpretable insights into how underlying mechanisms evolve over time. Because these forecasting approaches rely on causal structure, the thesis next turns to causal discovery. It develops an agentic framework using open-weight small language models that combines statistical evidence with agents for orienting, augmenting, and critiquing causal graphs. The framework is then extended with a causal story agent that reasons about the data-generating process to construct more accurate causal graphs. Collectively, this thesis advances causal AI toward systems that represent the mechanisms underlying observed outcomes rather than only statistical associations.