第三届全国统计与数据科学联合会议

2025-07-11 星期五

13:30-15:10 | IS001: Causal inference in observational studies IS001: Causal inference in observational studies

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编号	时间	类型	题目	讲者	单位
1	13:30-13:50	邀请报告	Causal Inference with Negative Controls when many negative control exposures exist	贾金柱	北京大学
		Invited Talk	Causal Inference with Negative Controls when many negative control exposures exist	Jinzhu Jia	Peking University
2	13:50-14:10	邀请报告	Imputation-Based Randomization Tests for Randomized Experiments with Interference	邓　柯	清华大学
		Invited Talk	Imputation-Based Randomization Tests for Randomized Experiments with Interference	Ke Deng	Tsinghua University
3	14:10-14:30	邀请报告	阶梯设计的高效设计型推断方法	Fan Xia	UCSF
		Invited Talk	Efficient Design-based Inference for the Stepped Wedge Design	Fan Xia	UCSF
4	14:30-14:50	邀请报告	Identification and estimation of causal peer effects using instrumental variables	罗姗姗	北京工商大学
		Invited Talk	Identification and estimation of causal peer effects using instrumental variables	Shanshan Luo	Beijing Technology and Business University
5	14:50-15:10	邀请报告	A generalized tetrad constraint for nonlinear models	英乃文	北京大学
		Invited Talk	A generalized tetrad constraint for nonlinear models	Naiwen Ying	Peking University

15:30-17:10 | IS002: Recent advances in network research IS002: Recent advances in network research

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编号	时间	类型	题目	讲者	单位
1	15:30-15:55	邀请报告	When can weak latent factors be statistically inferred?	Jianqing Fan	Princeton University
		Invited Talk	When can weak latent factors be statistically inferred?	Jianqing Fan	Princeton University
2	15:55-16:20	邀请报告	Counting Cycles with AI	金加顺	东南大学
		Invited Talk	Counting Cycles with AI	Jiashun Jin	Southeast University
3	16:20-16:45	邀请报告	A new spectral approach to dynamic network analysis	Tracy Ke	Harvard University
		Invited Talk	A new spectral approach to dynamic network analysis	Tracy Ke	Harvard University
4	16:45-17:10	邀请报告	Optimal Experimental Design for Neighborhood-Based Network Regression	Linglong Kong	University of Alberta
		Invited Talk	Optimal Experimental Design for Neighborhood-Based Network Regression	Linglong Kong	University of Alberta