preprints and publications

2022

  1. Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
    Thams, Nikolaj*, Oberst, Michael*, and Sontag, David
    *Equal contribution, order determined by coin flip
    In Advances in Neural Information Processing Systems (NeurIPS) 2022
  2. Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
    Thams, Nikolaj,¬†S√łndergaard, Rikke,¬†Weichwald, Sebastian,¬†and Peters, Jonas
    arXiv preprint arXiv:2203.06056 2022
  3. Invariant Ancestry Search
    Mogensen, Phillip,  Thams, Nikolaj, and Peters, Jonas
    In Proceedings of the 39th International Conference on Machine Learning 17‚Äď23 jul 2022

2021

  1. Local Independence Testing for Point Processes
    Thams, Nikolaj, and Hansen, Niels Richard
    arXiv preprint arXiv:2110.12709 2021
  2. Invariant Policy Learning: A Causal Perspective
    Saengkyongam, Sorawit,  Thams, Nikolaj, Peters, Jonas, and Pfister, Niklas
    arXiv preprint arXiv:2106.00808 2021
  3. Statistical Testing under Distributional Shifts
    Thams, Nikolaj, Saengkyongam, Sorawit, Pfister, Niklas, and Peters, Jonas
    Journal of the Royal Statistical Society, Series B 2021
  4. Regularizing towards Causal Invariance: Linear Models with Proxies
    Oberst, Michael,  Thams, Nikolaj, Peters, Jonas, and Sontag, David
    In Proceedings of the 38th International Conference on Machine Learning 18‚Äď24 jul 2021

2020

  1. Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
    Weichwald, Sebastian, Jakobsen, Martin E, Mogensen, Phillip B, Petersen, Lasse,  Thams, Nikolaj, and Varando, Gherardo
    In NeurIPS 2019 Competition and Demonstration Track 2020

2019

  1. Thams, N. (2019). Master thesis: Causal structure learning in multivariate point processes.