Causal Discovery for Cohort Data

• Applicant: Professorin Dr. Vanessa Didelez
• Subject Area: Epidemiology and Medical Biometry/Statistics ; Mathematics
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 389238860

The proposed project aims to capitalise on methods of causal discovery to supplement standard statistical analyses and hence fully exploit the potential wealth of information provided by cohort data. Cohort studies are a valuable resource for researchers, e.g. in epidemiology or sociology, when studying life-course developments so as to understand the relation between early exposures on later outcomes. Causal discovery is a field at the intersection of computer science and statistics. In its idealised form, it takes a dataset as input and outputs a graphical representation of the causal structure among the variables in the dataset, albeit relying on very specific assumptions. While these methods currently attract much attention, especially in the context of big data, to date, neither theory nor software are specifically targeted at, nor suitable for, cohort data - the proposed project intends to fill this gap. In particular we will (1) formulate and investigate a new class of causal models, cohort causal graphs (CCGs), and develop suitable and efficient model selection algorithms; (2) find new statistical approaches to address the particular challenges to causal discovery posed by typical cohort data, especially that of missing values; (3) develop guidelines, including recommendations and caveats, as well as user-friendly software for practical applications, so as to enable wide dissemination of the new methodology. We consider this a promising and worthwhile enterprise because causal discovery takes a radically different approach from traditional statistical analyses; it therefore has the potential to generate genuinely novel insights, including valuable suggestions for follow-up intervention studies which ultimately contribute to informing public health policies and medical decision making.

• DFG Programme: Research Grants