Causal inference has evolved into a robust and widely influential technology. Its primary advancements lie in the increasing power and practicality of its methods. Moreover, causal inference has become deeply integrated with AI, playing a critical role in improving its credibility and interpretability. It is now a crucial element in understanding large language models and has spurred significant scientific discoveries in fields such as biomedicine and climate science.
While challenges remain in integrating diverse data sources and ensuring the reliability of conclusions, causal inference has transcended its academic origins. It is now a reliable and indispensable tool for understanding the mechanisms of the world and facilitating rational decision-making.
Causal inference has shifted from being a cutting-edge academic theory to a key, practical tool used across various industries for data analysis. These advancements will be explored and discussed in greater depth at the upcoming PCIC conference.
The Pacific Causal Inference Conference (PCIC), established in 2019 by Dr. Xiao-Hua Zhou, Distinguished Chair Professor at Peking University, Chair of the Department of Biostatistics at the School of Public Health, and Director of the Biostatistics and Informatics Research Center at the Beijing International Center for Mathematical Research, has become an annual academic event in the causal science community. Dedicated to exploring the latest developments in causal inference across various domains, PCIC has successfully hosted seven editions in Beijing and Shanghai from 2019 to 2025. In 2025, PCIC was successfully held at Peking University, bringing together leading experts, scholars, and young researchers from the global causal inference. The conference further expanded its academic influence and propelled advancements in the field.
The 8th Pacific Causal Inference Conference (PCIC 2026) will be held in Tianjin, China from July 18 to 19, 2026. As an internationally influential academic platform, PCIC 2026 will convene top researchers from around the world, foster interdisciplinary collaboration, advance the frontiers of causal inference theory, and accelerate its innovative applications across diverse industries.
