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Søren Wen­gel Mo­gensen

Associate Professor

Emner
Matematik Statistik Kunstig intelligens Maskinlæring Data

Primary research areas

Statistics

I develop new methods in theoretical statistics and machine learning and apply them to challenging problems in engineering, epidemiology, and economics.

Causal inference and causal discovery

Questions relating to cause and effect are central in science. I work on data-driven methods that may give us a causal understanding of real-world phenomena (causal inference). I also work on methods for learning causal graphs from data (causal discovery).

Graphical models

Graphs are useful representations of statistical models. I develop methods for representing statistical dependencies and causal relations using graphs.

I study ways to go from data to knowledge

Many questions in science, everyday life, and business are causal in nature, and understanding cause and effect is important when making decisions. If we observe a positive correlation between wine consumption and living to old age, should we then recommend drinking a glass of wine daily? Income may be a so-called confounder: Perhaps people with above-average incomes drink more wine and live longer lives which could explain the observed correlation, even if there is no direct causal link between wine consumption and longevity. In this case, increasing wine consumption may not increase life expectancy.

In society and business, understanding the effects of potential actions is crucial for informed decision making. My research focuses on statistical methods for answering causal questions, especially in systems that evolve over time such as the climate or the economy.