Google Scholar

Preprints
  • S.W.Mogensen, Instrumental Processes Using Integrated Covariances, arXiv
Peer reviewed
  • S.Weichwald, S.W.Mogensen, T.E.Lee, D.Baumann, O.Kroemer, I.Guyon, S.Trimpe, J.Peters, N.Pfister, Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning, in Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR 176, 2022, link
  • A.Lund, S.W.Mogensen, N.R.Hansen, Soft Maximin Estimation for Heterogeneous Data, Scandinavian Journal of Statistics, 2022, link
  • S.W.Mogensen, Equality Constraints in Linear Hawkes Processes, in Proceedings of the 1st Conference on Causal Learning and Reasoning (CLeaR), 2022, pdf
  • S.W.Mogensen, N.R.Hansen, Graphical modeling of stochastic processes driven by correlated noise, Bernoulli 28(1), 3023-3050, 2022, link, arXiv, blog post
  • C. Øland, S.W.Mogensen, A.Rodrigues, C.S.Benn, P.Aaby, Reduced Mortality After Oral Polio Vaccination and Increased Mortality After Diphtheria-Tetanus-Pertussis Vaccination in Children in a Low-Income Setting, Clinical Therapeutics 43(1): 172-184, 2021, link
  • S.W.Mogensen, Causal screening in dynamical systems, in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, 2020, pdf, supp
  • S.W.Mogensen, N.R.Hansen, Markov equivalence of marginalized local independence graphs, Annals of Statistics 48(1), 2020, link, arXiv
  • S.W.Mogensen, D.Malinsky, N.R.Hansen, Causal learning for partially observed stochastic dynamical systems, in Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, 2018, pdf, supp, talk
  • P.Aaby, S.W.Mogensen, A.Rodrigues, C.S.Benn, Evidence of increase in mortality after the introduction of diphtheria-tetanus-pertussis vaccine to children aged 6-35 months in Guinea-Bissau: A time for reflection?, Frontiers in Public Health 6, 2018, pdf
  • S.W.Mogensen1, A.Andersen1, A.Rodrigues, C.S.Benn, P.Aaby, The introduction of diphtheria-tetanus-pertussis and oral polio vaccine among young infants in an urban African community: A natural experiment, EBioMedicine 17: 192-8, 2017, 1: equal contribution, pdf
  • C.B.Patsche, F.Rudolf, S.W.Mogensen, A.Sifna, V.F.Gomes, S.Byberg, C.Wejse, Low prevalence of malnourishment among household contacts of patients with tuberculosis in Guinea-Bissau, The International Journal of Tuberculosis and Lung Disease, 21(6), 2017, link
  • S.W.Mogensen, A.H.Petersen, A.Buchardt, N.R.Hansen, Survival prognosis and variable selection: A case study for metastatic castrate resistant prostate cancer patients, F1000Research, 5, 2016, pdf
  • S.W.Mogensen, P.Aaby, L.Smedman, C.L.Martins, A.Rodrigues, C.S.Benn, H.Ravn, The introduction of standard measles vaccination in an urban African community in 1979 and overall child survival: a reanalysis of data from a cohort study, British Medical Journal Open, 6(12), pdf, 2016
Talks
  • Instrumental processes using integrated covariances: An example of causal inference in engineering, Danish Data Science 2022, Billund, November 2022 (invited)
  • Instrumental processes using integrated covariances, Department of Statistics, School of Economics and Management, Lund University, October 2022 (invited)
  • Conditional instrumental variables in linear state-space models, Swedish Control Conference, Luleå, June 2022
  • Graphical structure learning and local independence in stochastic processes, Department of Informatics, Systems and Communication, University of Milano-Bicocca, April 2022 (invited)
  • Graphical structure learning in multivariate stochastic processes, DESY, Intelligent Process Control Seminar, virtual, January 2022 (invited)
  • Equality Constraints in Linear Hawkes Processes, Causal Inference Workshop at UAI, virtual, July 2021, talk, extended abstract, slides, poster
  • Estimation in Hawkes processes as a missing data problem, EcoSta2021, virtual, June 2021 (invited)
  • Causal screening for dynamical systems, UAI-20, virtual, August 2020
  • Causal models of Hawkes processes, Causal inference working group, Department of Biostatistics, Johns Hopkins University, December, 2019 (invited)
  • Graphical modeling of local independence in dynamical systems, Department of Statistics, University of Washington, November 2019 (invited)
  • Causal models of Hawkes processes, Causal inference working group, Department of Statistics, University of Seattle, October, 2019
  • Causal learning for linear SDEs, EuroCIM, Bremen, March 2019
  • Graphical modeling of Ornstein-Uhlenbeck processes, ASC2019, Tokyo, February 2019 (invited)
  • Marginalized local independence graphs, CMStatistics, Pisa, December 2018 (invited)
  • Causal learning of partially observed stochastic dynamical systems, UAI-18, Monterey, August 2018, talk
  • Structure learning and linear stochastic differential equations, Nordstat, Tartu, June 2018 (invited)
  • Markov equivalence in graphical models for partially observed stochastic processes, ACIC, Pittsburgh, May 2018
  • Markov equivalence in graphical models for partially observed stochastic processes, Symposium on Statistics in Complex Systems, Copenhagen, April 2018 (invited)
Other
  • Kausal inferens: En historie om faldgruber ved prædiktion (in Danish) talk at ASTIN (a chapter of Den Danske Aktuarforening) giving an introduction to causal inference, November 2022
  • UAI 2022 Tutorial, Graphical Models Meet Temporal Point Processes (with Debarun Bhattacharjya, Abir De, and Tian Gao), slides
  • PhD thesis, Graphical modeling in dynamical systems, pdf
  • Poster presentation: Sparse maximin aggregation of neuronal activity, SPARS, Lisbon, June 2017, extended abstract