Identifiability of Causal Graphs using Functional Models

This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more easily on given data. We further provide a practical algorithm that recovers the causal graph from finitely many data; experiments on simulated data support the theoretical findings

Nationalparkausweisungen in Deutschland zwischen 1966 und 2009 : ökologische Evidenzen oder politische Ideen? : Zusammenhänge zwischen ökologischer Forschung und Nationalparkkonzepten

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Collaboratively and at scale: lending CAPE’s experience to the challenge of describing knowledge mobilisation

Across Higher Education Institutions (HEIs) and in policy domains, there has been increased support for and investment in knowledge mobilisation activities and roles. At a time in which funding decision makers and awardees need to evidence the value of investments, questions arise: what is knowledge mobilisation, what does it do and why does it need investing in? Through our work in CAPE, we are seeking to contribute insight into the ways that academic policy engagement is enacted through knowledge mobilisation. We reflect on what our experience of knowledge mobilisation practice collaboratively and at scale tells us, and why a deeper appreciation of the way it works at systemic levels might be useful for the sector as it develops