preprints and publications

2024

  1. Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
    Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, and Jonas Peters
    arXiv preprint arXiv:2203.06056 (To appear in JMLR), 2024
    arXiv Code
  2. Local Independence Testing for Point Processes
    Nikolaj Thams, and Niels Richard Hansen
    IEEE Transactions on Neural Networks and Learning Systems, 2024
    arXiv HTML Code
  3. Family-based preventive intervention for children of parents with severe mental illness: A randomized clinical trial
    Anne Dorothee Müller, Ida Christine Tholstrup Gjøde, Nikolaj Thams, Sidsel Ingversen, Mala Moszkowicz, Jens Richardt Møllegaard Jepsen, Lisbeth Juhl Mikkelsen, Signe Sofie Nielsen, Nicoline Hemager, Merete Nordentoft, and Anne A. E. Thorup
    JCPP Advances, 2024
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2023

  1. Invariant Policy Learning: A Causal Perspective
    Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, and Niklas Pfister
    IEEE transactions on pattern analysis and machine intelligence, 2023
    arXiv HTML Code
  2. Statistical Testing under Distributional Shifts
    Nikolaj Thams, Sorawit Saengkyongam, Niklas Pfister, and Jonas Peters
    Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023
    arXiv HTML Code

2022

  1. Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
    Nikolaj* Thams, Michael* Oberst, and David Sontag
    In Advances in Neural Information Processing Systems (NeurIPS), 2022
    *Equal contribution, order determined by coin flip
    arXiv HTML Code
  2. Invariant Ancestry Search
    Phillip Mogensen, Nikolaj Thams, and Jonas Peters
    In Proceedings of the 39th International Conference on Machine Learning, 17–23 jul 2022
    arXiv HTML Code

2021

  1. Regularizing towards Causal Invariance: Linear Models with Proxies
    Michael Oberst, Nikolaj Thams, Jonas Peters, and David Sontag
    In Proceedings of the 38th International Conference on Machine Learning, 18–24 jul 2021
    arXiv HTML Code

2020

  1. Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
    Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen, Lasse Petersen, Nikolaj Thams, and Gherardo Varando
    In NeurIPS 2019 Competition and Demonstration Track, 18–24 jul 2020
    arXiv HTML Code

2019

  1. Master thesis: Causal structure learning in multivariate point processes
    Nikolaj Thams
    18–24 jul 2019
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