Causal inference

Causal inference aims to deepen the understanding of social phenomena, or of analyses of efficiencies of different treatments (such as, medicinal, work-life or environmental treatments). Statistics cannot by itself create knowledge of various phenomena, but is rather used to test theories under various assumptions. Thematic theory, together with statistical theory and knowledge of how data is collected, jointly form the causal analysis. For that reason, it is necessary to understand thematic questions, and that in the development of new methods jointly consider how data together with statistical theory can improve analysis of causal questions, such as testing of theories as well as pure effect evaluations.

Currently, we are working with researchers in medicine, economics, psychology and engineering. Some of our thematic work include: (i) test of theories for how mass media affect voters' ability to hold elected officials accountable, (ii) analysis of the effect of family friendly workplaces on wages and income for men and women, (iii) analysis of the effect of air pollution on childrens' health, (iv) test of gender differences in preferences and (v) analysis of electricity consumption and changes in electricity tariffs and consumer information related to energy savings. Methodologically, we have made contributions to, among others, the design of randomized experiments and identification of causal effects using observational data, and register data in particular. Examples of problems we have investigated concern situations when the timing of a treatment is a choice (as opposed to a randomized experiment when the time for intervention is the same for all treated and untreated individuals) and where there might exist measurement errors, both in control variables and in the timing of treatment.

Responsible researchers: Per Johansson and Ingeborg Waernbaum

Last modified: 2021-08-31