报告人：张寄冀
报告地点：腾讯会议ID: 940379287
报告时间：2024年12月05日星期四14:00-15:00
报告摘要：
Directed acyclic graphical (DAG) models are one of the most widely used frameworks for causal modeling. In this talk, we present a category-theoretic treatment of DAG models by associating each DAG with a free Markov category in a canonical way. This framework enables us to study key concepts in causal reasoning from an abstract perspective, including causal independence/separation, causal conditionals, and decomposition of intervention effects. Our results abstract away from the specifics of common causal models such as causal Bayesian networks, making them both more widely applicable and conceptually clearer. Notably, we show that the 'causal core' of the celebrated do-calculus can be unified in a single principle of modularity.
主讲人简介：
张寄冀本科就读北京大学哲学系，后于卡耐基梅隆大学哲学系获得博士学位。先后任教于加州理工学院，香港岭南大学，香港浸会大学，也曾担任华为诺亚方舟人工智能实验室因果研究组的学术顾问。现任香港中文大学哲学系教授，文学院副院长(研究)。 主要研究领域包括科学哲学和形式知识论，同时从事因果知识表征和学习的跨学科研究。其成果不仅发表在哲学领域的一流期刊, 也发表在人工智能领域的顶刊和顶会。多个研究课题获香港研究资助局资助，包括人文学及社会科学杰出学者基金。
