This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention\u2011effect estimation, (2) causal graph analysis, and (3) detection of causal mechanism shifts and change points. First, for causal inference, we consider procedures in which generative models align domain knowledge with observational signals to represent treatment, confounding, and temporal context. This enables stable estimation of intervention effects and principled policy evaluation without relying on explicit counterfactual generation. Second, for causal graph analysis, we outline strategies that combine language\u2011grounded knowledge extraction and constraint proposals with statistical checks to improve the reliability of directionality and structure, yielding interpretable hypothesis spaces and testable causal claims. Third, for shift detection, we describe methods that disentangle changes in functional mechanisms from changes in noise, supporting early diagnosis of performance degradation, targeting of monitoring resources, and evidence\u2011based model updates in deployed settings. Across these tasks, generative AI serves as a computational aide for knowledge alignment, hypothesis proposal and pruning, uncertainty annotation, and experiment\u2011design suggestions. We conclude with a brief outlook on a causal agent that orchestrates data ingestion, hypothesis formation, intervention\u2011effect estimation, shift monitoring, and policy revision, offering an integrated, yet auditable and modular, workflow for reliability\u2011centered decision support.<\/p>\n","protected":false},"excerpt":{"rendered":"
This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention\u2011effect estimation, (2) causal graph analysis, and (3) detection … <\/p>\n