Causal data-driven insight and prediction in care

PhD with Integrated Studies in Advanced Care

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the Advanced Care Research Centre (ACRC), a new multi-disciplinary research centre at the University of Edinburgh. The ACRC’s  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.

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.

There is an initial deadline to apply of 5 May 2021. This will be extended if the project has not been recruited to.  

Application forms are now available here:
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It is essential to read the How to Apply section of our website before you apply:
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Further Information: 


The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here:

Research Group
1. Bernhard Schölkopf, Causality for Machine Learning,
2. Louizos et al, Causal Effect Inference with Deep Latent-Variable Models, NIPS 2017
3. Schölkopf et al, Towards Causal Representation Learning, Proceedings of the IEEE, Special issue on Advances in Machine Learning and Deep Neural Networks, 2021.

Closing Date: 

Monday, June 28, 2021

Principal Supervisor: 

Assistant Supervisor: 

Prof Ewen Harrison


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.

•    Comfortable working in a multidisciplinary environment where clinical priorities influence the direction of technology research.
•    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.  
•    Motivated to develop leadership potential


Tuition fees + stipend of £16,500 are available for Home students.

There is a scholarship for international students which covers tuition fees and stipend, and which is awarded competitively.

Further information and other funding options.

Informal Enquiries: