Submit a Manuscript to the Journal
Biostatistics & Epidemiology
For a Special Issue on
PCIC 2024
Manuscript deadline
31 December 2024
Special Issue Editor(s)
Jinzhu Jia,
Department of Biostatistics, Peking University, China
[email protected]
PCIC 2024
Biostatistics & Epidemiology is hosting a special issue for the 6th Pacific Causal Inference Conference (PCIC 2024) and encourages the submission of full papers associated with PCIC 2024.
Causal inference has gained popularity in statistics, biostatistics, biomedical science, computer science, economics, epidemiology, and various social sciences. Prof. Zhou, as the founding Chair of the PCIC organizing committee, will be hosting this conference for the 5th time. The conference aims to share the latest developments in causal inference by inviting university-based statisticians/professors and industry-based statisticians/scientists. After two years of online meetings, this year, the Pacific Causal Inference Conference will be held as a hybrid event in Shanghai on July 5th and 6th.
Topics of interest for PCIC 2024 include, but are not limited to:
T1. Frontiers in Causal Inference Theories and Methods
- Causal discovery methods
- Estimation of average causal effects
- Estimation of conditional average causal effects
- Estimation of individual causal effects and counterfactual learning
- Instrumental variable methods
- Methods for causal representation learning
- De-biasing methods based on causal inference
- Causal inference based on uncertainty
- Causal interpretability
- Causal assessment, tools, and resources
- Modeling unmeasured confounding factors in complex scenarios
T2. Applications of Causal Inference in the Field of Artificial Intelligence
- Causal reasoning methods based on Large Language Models (LLM)
- Causal discovery methods based on Large Language Models (LLM)
- Causal natural language processing
- Causal precision medicine
- Causal recommendation systems
- Causal trustworthy learning
- Causal strategy learning
- Causal methods for addressing out-of-distribution generalization (OOD)
- Causal-based counterfactual data generation
T3. Computational Intelligence and Causal Inference
- Unsupervised and Semi-supervised Deep Learning Connected to Causality
- Causal Generative Models for Machine Learning
- Machine Learning Algorithms for Causal Discovery
- Machine Learning Building on Causal Principles
- Reinforcement Learning and Causal Inference
- Causal inference in Natural Language Processing
- Causal Reasoning and Large Language Models
- Data Mining for Causality Methods
T4. Specific Application of Causal Inference
- Applications in online systems (e.g. search, recommendation, ad placement)
- Applications in physical systems (e.g. cars, smart homes)
- Applications in medicine (e.g. personalized treatment, clinical trials)
- Applications in economics and political science
- Causal inference in philosophy and psychology
- Other Real-world problems for causal analysis
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