JST CREST [信頼されるAIシステム] 信頼されるAIシステムを支える基盤技術
プロジェクト名: 信頼されるAIシステムを実現するための因果探索基盤技術の確立と応用
研究代表者: 清水昌平


JST CREST  Trusted quality AI systems
Causal discovery and its applications for reliable AI
Research Director: SHIMIZU Shohei

Statistical causal inference using causal graphs is essential in improving the explainability, fairness, and performance needed for reliable AI. To perform statistical causal inference, a causal graph needs to be drawn by the analyst, but it often is the case that insufficient domain knowledge is available for this purpose. Then, a causal structure search methodology that uses data to infer a causal graph, i.e., causal discovery, is useful. Thus, we develop methods for causal discovery to infer causal graphs from data and use them to analyze explainability and fairness in the four areas of policy science, environmental studies, preventive medicine, and clinical medicine.