Machine learning and reliable inferences

This working package will produce state-of-the-art machine learning models to be used in public health, but also a more general understanding of the process of model building. The underlying basic questions are the following: How do different inferential goals, like predictive modelling, causal inference, or algorithmic decision-making, influence the requirements set on the data? Second, how do the messiness and various possible deficiencies of register data – missing or unobservable data, sample distortion, limited variation, changing definitions, etc. – affect the uncertainty of the inferences that can be made from it? Third, what is the role of background knowledge and expert judgment in building and developing machine learning models?

Principal investigator

Pekka Marttinen

Assistant Professor

Machine Learning

Aalto University

Link to the researcher profile

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Related scientific publications

Cui, T., Havulinna, A., Marttinen, P.*, and Kaski, S.* (2021). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 1-31.

Sun, W, Ji, S., Cambria, E., and Marttinen, P. (2021). Multitask recalibrated aggregation network for medical code prediction. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

Rissanen, S. and Marttinen, P. (2021). A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021).