会议摘要（Abstract）

Causality lies at the heart of scientific discovery and data-driven decision-making. While the philosophical foundations of causality span centuries, recent advances in statistics and machine learning have revolutionized our ability to measure the effects of interventions—from randomized trials to large-scale observational data.  

The past two decades have seen tremendous growth in causal analysis methods, powered by interactions between statistical theory, machine learning algorithms, and domain-specific applications. These developments are now influencing fields such as healthcare, economics, social sciences, and artificial intelligence. 

This workshop aims to bring together researchers and practitioners in causal analysis as well as related areas in the field of statistics and machine learning to discuss emerging challenges in these fields. This workshop will emphasize both theoretical rigor and practical implementation, fostering dialogue across disciplines to tackle the challenges of causality in complex, data-rich environments. 

举办意义(Description of the aim)

The Sanya Workshop on Causality and Machine Learning aims to bring together leading researchers and practitioners to discuss cutting-edge advances at the intersection of causal methodology and machine learning. By focusing on both theoretical innovation and real-world applications—from complex randomized trials to causal deep learning—the workshop will serve as a platform to: i) exchange ideas on emerging challenges, such as high-dimensional causal inference, interpretable AI for decision-making, and scalable causal algorithms; ii) bridge disciplines by creating dialogues between statisticians, machine learning researchers, and domain experts in healthcare, economics, and policy; and iii) empower collaborations between domestic and international scholars. 

Held in the vibrant setting of Sanya, the workshop will combine rigorous scientific discussion with a collaborative atmosphere, aiming to enrich China’s causality community and inspire future international partnerships.