Causal data-driven insight and prediction in care

   School of Engineering

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof Sotirios Tsaftaris, Prof E Harrison  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Please ensure you check the eligibility criteria before applying to this project.

The Advanced Care Research Centre (ACRC) is a new, multi-disciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”.

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, 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.   


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. 

You must read How to apply prior to application

Please Apply here

Funding Notes

PhDs are funded with an enhanced stipend for the full 4 year period.
The call is open to candidates of any nationality but funded places for overseas nationals will be strictly limited to 4 international students who can apply for the highly competitive ACRC Global Scholarship.
It is essential to read the How to Apply section of our website before you apply:
Please apply here:


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.
Research Group
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs