美国制裁、双轮驱动与中国关键核心技术突破——基于双重机器学习的因果推断

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Abstract This paper takes the U.S.sanctions on China's chip industry as the external impact,adopts the panel data of Chinese A-share listed companies from 2015 to 2021,empirically examines the impact of the U.S.sanctions on the breakthrough of key core technologies of China's chip enterprises through theoretical derivation combined with double machine learning model,and at the same time,probes into the stage characteristics of the breakthrough of key core technologies and the differences in the stage effects of the impact.Relying on the mission-driven theory and vision-driven theory,the study further examines the driving mechanism under the dual perspective of government and enterprises with the help of machine learning causal effect estimation method.The main conclusions of the study are as follows.First,the U.S.sanctions backfired,triggering Chinese chip firms to innovate.The innovation effect is stronger in state-owned,large-scale and IPR-protected firms.Second,the key core technology breakthrough is a process from the basic research stage to the technological breakthrough stage to the value embodiment stage,the impact of the U.S.sanctions on the three stages of the stage with the increasing difficulty of the stage in order to weaken.Third,government mission-driven and enterprise vision-driven are important channels for resisting U.S.sanctions and reaching innovation incentives.In terms of stages,the two-wheel drive mechanism has a significant effect on innovation in the basic research and technological breakthrough stages,while for the value embodiment stage across the R&D period,it is necessary to find other mechanisms for incentive continuity.

Key words： Key core technology breakthrough U.S.technology sanction Two-wheel drive Double machine learning

Received: 20 March 2024

PACS:	F279.23
	F062.4

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	Articles by authors
	Cao Yuxuan
	Yue Li
	Pan Junbai

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Cao Yuxuan,Yue Li,Pan Junbai. U.S.Sanctions,Two-Wheel Drive and China's Key Core Technology Breakthrough——Causal Inference Based on Dual Machine Learning[J]. , 2025, 6(3): 164-176.

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http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2025/V6/I3/164

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