会议摘要（Abstract）

Causality has long been central to the human philosophical debate and scientific pursuit. Among the many relevant questions on causality, statistics arguably can contribute the most to the question of measuring the effects of “causes”, or more specifically, interventions or actions. The last two decades have witnessed an explosive growth in statistical and machine learning theory and methods for causal inference. These methods have been increasingly applied to solve real world problems in many disciplines. This workshop will focus on exchanging new developments in theory and methods as well as impactful interdisciplinary applications. Main topics will include: (i) design and analysis of complex randomized experiments; (ii) natural and quasi-experimental designs, including instrumental variables; (iii) machine learning methods for causal inference; (iv) causal inference in action; (v) accessible causal inference: software and translational work.

举办意义(Description of the aim)

This workshop aims to bring together a group of respected and active researchers to present their ongoing work in several most important current areas of causal inference, including complex randomized experiments, natural experiments, machine learning methods, software development, and interdisciplinary applications. It will provide an opportunity for researchers at different career stages to exchange ideas with internationally leading experts and foster new collaborations in an intimate environment. In particular, the workshop will provide a platform for young researchers to showcase their achievements and network. Overall, the workshop is expected to contribute to building and strengthening the causal inference community in China.