Causal data-driven insight and prediction in care

This project sits within the ACRC Academy, a dedicated Centre for Doctoral Training, co-located with the Advanced Care Research Centre, whose students will deliver key aspects of the ACRC research agenda through a new doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors.

The PhD with Integrated Study in Advanced Care is a novel, structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business and commerce, social work, medicine and related health and care professions. This unique level of diversity is a key attribute of our programme.



To develop causal models that capture statistical association and develop these associations into insights that can inform future care decisions. 


  • To develop causal models that inform caring decisions and interventions. 
  • To develop a framework of embedding multimodal sources of information clinical insight and knowledge into causal models. 
  • To demonstrate that such models offer more transparent, fair and unbiased predictive models. 


The ultimate decision for a carer is to predict an intervention’s outcome e.g. medication, diet etc. An elegant framework for such tasks, is causal machine learning [1]. This project will start by integrating data being made available by the ACRC for an exemplar caring decision and use a simple causal predictive model to develop a demo application. As simple models cannot scale as the number of information sources increase, non-linear causal models will then be developed [2].  This will require causal structure discovery: finding useful variables and their causal associations. To address this, we will combine representation learning and causality [3].  A key desire for any AI is to be fair and transparent. While causal models by definition should be explainable, we will study whether predictive models based on causality do reduce risks of bias and increase fairness and transparency.   

Further Information: 

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Closing Date: 

Saturday, April 1, 2023

Principal Supervisor: 

Assistant Supervisor: 


Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

We are looking for an enthusiastic PhD student who can work across disciplinary boundaries. The student should be comfortable with computational methods and machine learning and have exposure to developing data science approaches. Equally important is an open attitude to co-creation of research solutions in order to create solutions that are fair and equitable and consider the several dimensions of use of AI in care.

We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application.


Tuition fees + stipend are available for Home/EU and International students

Further information and other funding options.

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