##### Preprints

**S.W.Mogensen**,*Weak equivalence of local independence graphs*, arXiv**S.W.Mogensen**, O.O.Aalen, S.Strohmaier,*Time-dependent mediators in survival analysis: Graphical representation of causal assumptions*, arXiv**S.W.Mogensen**,*Faithful graphical representations of local independence*, arXiv**S.W.Mogensen**, K.Rathsman, P.Nilsson,*Causal discovery in a complex industrial system: A time series benchmark*, arXiv

##### Peer reviewed

- A.Bregoli, K.Rathsman, M.Scutari, F.Stella,
**S.W.Mogensen**,*Analyzing complex systems with cascades using continuous-time Bayesian networks*, in Proceedings of the 30th International Symposium on Temporal Representation and Reasoning (TIME), 2023, link **S.W.Mogensen**,*Instrumental Processes Using Integrated Covariances*, in Proceedings of the 2nd Conference on Causal Learning and Reasoning (CLeaR), 2023, pdf- 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 **S.W.Mogensen**, N.R.Hansen,*Graphical modeling of stochastic processes driven by correlated noise*, Bernoulli 28(4): 3023-3050, 2022, link, arXiv, blog post**S.W.Mogensen**,*Equality Constraints in Linear Hawkes Processes*, in Proceedings of the 1st Conference on Causal Learning and Reasoning (CLeaR), 2022, pdf- A.Lund,
**S.W.Mogensen**, N.R.Hansen,*Soft Maximin Estimation for Heterogeneous Data*, Scandinavian Journal of Statistics, 2022, link - 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**, N.R.Hansen,*Markov equivalence of marginalized local independence graphs*, Annals of Statistics 48(1), 2020, link, arXiv**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**, 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 - 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**^{1}, A.Andersen^{1}, 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**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(2680), 2016, pdf**S.W.Mogensen**, P.Aaby, L.Smedman, C.L.Martins, A.Rodrigues, C.S.Benn, H.Ravn,*Introduction of standard measles vaccination in an urban African community in 1979 and overall child survival: A reanalysis of data from a cohort study*, BMJ Open, 6(12):e011317, pdf, 2016

##### Talks

- Graphical models of local independence in stochastic processes, Statistics Seminar, EPFL, Lausanne, October 2023
**(invited)** - Learning causal graphs of multivariate stochastic processes from tests of local independence or Granger causality, Causal inference for time series data (workshop @ UAI 2023), Pittsburgh, August 2023, slides
**(invited)** - Learning Granger-causal graphs from partially observed multivariate time series, Nordstat, Gothenburg, June 2023
- Instrumental processes using integrated covariances, EuroCIM, Oslo, April 2023
- Learning Granger-causal graphs from partially observed multivariate time series, Data Science and AI seminar, Department of Computer Science and Engineering,
Chalmers and University of Gothenburg, March 2023
**(invited)** - 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 Danish 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