Who am I? My name is Jenny Häggström and I am currently employed as an Associate Professor at the Department of Statistics, USBE. I have been at Umeå University since 2001, when I was a first-year student. My undergraduate studies consisted of statistics, math, economics, business and administration, informatics and philosophy of science. I had originally planned to major in either business and administration or economics, but already after my first statistics course I changed my mind and enrolled in the Statistics programme. I was fascinated by what data could tell you and enjoyed learning about the mathematical underpinnings.
During my time as an undergraduate student my vision of the future was being employed as a statistician outside academia, perhaps in the private sector. As it were, I changed my mind again and (encouraged by my Master thesis supervisor) I applied for, and was appointed, a PhD student position. Under the guidance of Professor Xavier de Luna I produced the thesis “Selection of Smoothing Parameters with Application in Causal Inference”. In the thesis I touch upon a number of different statistical issues but the focus is on non-parametric regression methods and methods for estimating causal effects from observational (i.e., non-randomized) studies.
After finishing my PhD my research has continued to concern estimation of causal effects from observational data and in particular covariate selection for this specific purpose. Within this field my interest lies in developing and applying methods for which the required assumptions of the underlying data generating process are as few as possible, which can handle high-dimensional data and have “good” statistical properties. To achieve the aforementioned goals one most likely needs to use what is sometimes called “machine learning”, i.e. data-driven model-free methods for prediction or estimation. In 2013 I applied (sole applicant and PI) for funding from the Swedish Research Council (VR) and was one of the lucky few that got an approval. The project “Methods for improving estimation of causal effects in observational studies” received 785 000 SEK/year during 2014-2016.
From 2014 I have been funded part time by the Umeå SIMSAM Lab for administrating their database, handling researchers’ applications for data and making data retrievals based on the approved applications. The Umeå SIMSAM Lab was established, with funds from the Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical sciences (SIMSAM), to perform high quality, interdisciplinary microdata research on childhood and its relationship with lifelong health and welfare, focusing on areas of societal importance. Umeå SIMSAM Lab has an exceptional microdata infrastructure linking data on an individual level from Statistics Sweden, the National Board of Health and Welfare as well as other national and regional registers.
In 2016 I spent three months as a visiting scholar at the Division of Biostatistics at University of California Berkeley with Professor Mark van der Laan as faculty sponsor.
In 2017 I was appointed as Associate Professor in Statistics and also received the USBE Scientific Award in Statistics.
Outside of work I spend most of my time with my husband and our two children. I love reading, like solving crosswords and is ok with watching superhero movies.