LabCompass

Arvid Sjölander

Mathematics · Indiana University

Publications

321

Citations

8,397

Est. group size

Recurring co-author estimate

Active years

21

Publishing since 2006

Research summary
AI-generated

Arvid Sjölander develops statistical methods for causal inference, which is the study of how to draw cause-and-effect conclusions from data rather than just correlations. Much of this work is applied to medical and epidemiological questions, such as evaluating treatments, understanding disease risks, and using family or sibling comparisons to isolate causes. The research combines theoretical tools (like directed acyclic graphs and Bayesian networks) with practical applications in health and clinical research.

Causal inference methodsEpidemiology and health data analysisStatistical methods for clinical trialsBayesian networks and probabilistic modelingFamily-based and sibling study designs

Publication activity has grown over the last decade, rising from around 7-18 papers per year in 2017-2019 to roughly 30-37 per year in recent years.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence
Publications per year over the last 10 years — averaging 29.4/year recently
2017: 18 publications172018: 7 publications182019: 17 publications192020: 27 publications202021: 23 publications212022: 33 publications222023: 33 publications232024: 30 publications242025: 37 publications37252026: 14 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×17
  • Nephrology Dialysis Transplantation×16
  • Figshare×12
  • European Journal of Epidemiology×11
  • Epidemiology×9

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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