Who am I? My name is Jenny Häggström, and I am currently an Associate Professor in the Department of Statistics, Umeå School of Business, Economics and Statistics. I have been at Umeå University since 2001, beginning as a first-year student. My undergraduate studies included statistics, mathematics, economics, business administration, informatics and philosophy of science. I had initially planned to major in either business administration or economics but, after my first statistics course, I changed my mind and enrolled in the Statistics program. I was fascinated by the insights data could provide and enjoyed learning about the mathematical underpinnings.

During my time as an undergraduate student, I envisioned my future as a statistician outside academia, possibly in the private sector. However, I changed my mind again and, encouraged by my Master thesis supervisor, applied for — and was appointed to — a PhD student position. Under the guidance of Professor Xavier de Luna, I completed the thesis “Selection of Smoothing Parameters with Application in Causal Inference”. In my thesis, I addressed various statistical issues, with a focus on non-parametric regression methods and methods for estimating causal effects from observational (i.e., non-randomized) studies.

After completing my PhD, my research has continued to focus on estimating causal effects from observational data, and in particular model and covariate selection for this purpose. My primary interest, within this field lies in developing and applying methods that require minimal assumptions about the underlying data-generating process,  can handle high-dimensional data, and have “good” statistical properties. Achieving these goals often involves using machine learning methods. In 2013, I applied (as sole applicant and PI) for funding from the Swedish Research Council (VR) and was fortunate to receive approval. The project, titled “Methods for improving estimation of causal effects in observational studies”, was awarded 785 000 SEK per year for 2014-2016.

Since 2014, I have been partially funded by the Umeå SIMSAM Lab to administer their database, handle researchers’ data applications, and retrieve data based on the approved requests. The Umeå SIMSAM Lab was established with funding from the Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical sciences (SIMSAM) to conduct high quality, interdisciplinary microdata research on childhood and its relationship to lifelong health and welfare, focusing on areas of societal importance. Umeå SIMSAM Lab has an exceptional microdata infrastructure linking data, linking individual-level data from Statistics Sweden, the National Board of Health and Welfare, and 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 my faculty sponsor.

In 2017, I was appointed as Associate Professor in Statistics and also received the USBE Scientific Award in Statistics.

In 2018, my project “Methodological develpment for estimating marginal causal effectss of non-randomized treatments on time-to-event outcomes” was awarded 1 250 000 SEK per year for 2019-2022.

In 2019-2024, I served as the main supervisor for PhD student Guilherme Wang de Faria Barros, who defended his thesis “Estimation of hazard ratios from observational data with applications related to stroke” on February 2, 2024.

Outside of work, I spend most of my time with my family. I love reading, watching “På spåret”, running (slowly), and doing yoga.

« CV