Publications

Peer-Reviewed Publications

  1. Cao C. (2026). Challenges and recent advances in visual and haptic feedback for AI-enhanced teleoperated robot-assisted surgery. PLOS Digital Health, forthcoming. [Preprint]
  2. Zhang C, Cao C, Zhang P, Alvarez RM, Debnath R. (2026). Inequitable efficiency: Unravelling the social and built environment drivers of London’s housing energy performance. Energy Policy, 210, 115057.
  3. Cao C, Dyrstad JM, Green CP. (2025). Modeling impacts of traffic, air pollution, and weather conditions on cardiopulmonary disease mortality. Scandinavian Journal of Public Health, 53(2), 119–124.
  4. Cao C, Debnath R, Alvarez RM. (2025). Interaction between climate factors and air quality in three Norwegian cities: A machine learning analysis. Atmospheric Environment: X, 100366.
  5. Cao C, Debnath R, Alvarez RM. (2025). Physics-based machine learning for predicting urban air pollution using decadal time series data. Environmental Research Communications, 7(5), 051009.
  6. Cao C. (2024). How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches. Journal of Economic Behavior & Organization, 221, 544–569.
  7. Cao C. (2024). Integration of ten years of daily weather, traffic, and air pollution data from Norway’s six largest cities. Scientific Data.

Preprints and Under Review

  1. Cao C. (2026). Where the Data End and the Mechanism Begins: Doubly Robust Causal Digital Twins for Treatment Strategies. arXiv.
  2. Cao C. (2026). When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations. arXiv.
  3. Cao C, Kyriakides TC, Vrasidas P. (2026). Different representation learning objectives recover distinct latent structures from the same psychometric data. arXiv.
  4. Cao C. (2026). When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation. arXiv.
  5. Cao C. (2026). Causal Inference under Interference with Learned Exposure Mappings. arXiv.
  6. Cao C, Ma S. (2026). AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program. arXiv.
  7. Cao C, Ma S. (2026). AI-Assisted Causal Inference and Mediation Analyses of Environmental and Psychosocial Determinants of Subjective Cognitive Difficulties in the All of Us Research Program. arXiv.

Data Resources

Citation metrics are available on my Google Scholar profile.

Research Leadership and Funding

Google Cloud Academic Research Grant Recipient
Google Cloud · 2026

Awarded cloud computing resources through the Google Cloud Academic Research Grants program to support research in causal artificial intelligence for precision medicine.

Lead Investigator, Approved MWCCS Scientific Concept (X24047)
MACS/WIHS Combined Cohort Study (MWCCS) · 2024–2032

Awarded project: Using causal machine learning to investigate how climate change, air pollution, and genetic factors shape biological aging among people with HIV.

Competitive Research Funding Recipient
Computational Economics and Optimization, Norwegian Research Council · 2022

Supported research activities and a visiting scholarship at the University of Pennsylvania.