Stijn Vansteelandt
researcher
Facts
10 properties- Instance of
- human
- Date of birth
- 1976
- Occupation
- biostatistician
- Employer
- Ghent University[3]
- Educated at
- Ghent University[4]
- Doctoral student
- Oliver Dukes[6]
- Imported from
- wholeness_audit_2026-05-02
- Maintained by WikiProject
- WikiProject Mathematics[8]
🏷️ Also known as
en Stijn Vansteelandt
🔖 Identifiers · 13
- ORCID iD
- 0000-0002-4207-8733
- VIAF cluster ID
- 133150746967016300580
- OpenAlex ID
- A5041323870as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Dimensions author ID
- 0637036363.16
- Gateway to Research person ID
- 59C812F8-D860-466C-B279-8F194C34C18E
- Google Knowledge Graph ID
- /g/11f0z5blvy
- Google Scholar author ID
- 734J_XAAAAAJ
- IdRef ID
- 204204852
- MR Author ID
- 672771
- Publons author ID
- 2096860
- ResearcherID
- J-4072-2017
- Springer Nature person ID
- 0637036363.16
Show all 13 identifiers +1
- ZbMATH author ID
- vansteelandt.stijn
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 16
Assumption-lean Inference for Generalised Linear Model Parameters,
Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models,
Confounder selection strategies targeting stable treatment effect estimators,
Data-Adaptive Bias-Reduced Doubly Robust Estimation,
Demystifying Statistical Learning Based on Efficient Influence Functions,
Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.,
Doubly robust methods for handling confounding by cluster,
Frequentist versus Bayesian approaches to multiple testing
+8 more
← Authored by 16
Assumption-lean Inference for Generalised Linear Model Parameters,
Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models,
Confounder selection strategies targeting stable treatment effect estimators,
Data-Adaptive Bias-Reduced Doubly Robust Estimation,
Demystifying Statistical Learning Based on Efficient Influence Functions,
Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.,
Doubly robust methods for handling confounding by cluster,
Frequentist versus Bayesian approaches to multiple testing
+8 more
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