Welcome!

Hi! I am Damian Machlanski - a Research Associate in Causal AI at the University of Edinburgh and a Postdoctoral Fellow at the Causality in Healthcare AI Hub (CHAI). I am interested in all things related somehow to causality, including mathematical theory, statistical methods, applications with real data, and also the overarching philosophy around it. My research in causality often intersects with machine learning and observational data, leading to my specific interests in causal discovery and causal effect estimation, but also domain generalisation and model selection. Practical applications of those methods to healthcare datasets, which are often temporal in nature, is something I have been also working on since joining CHAI.

Previously to joining CHAI and moving to Edinburgh, I have obtained my PhD in Computer Science at the University of Essex (Colchester), supervised by Dr Spyros Samothrakis and Prof. Paul Clarke. Prior to my PhD studies, I worked as a Software Developer for several years (I still enjoy programming in my work!).

Updates

• My last PhD paper on the challenges of hyperparameter tuning for causal effect estimation is finally out with Machine Learning!
• New paper on explainable AI to improve transparent predictive risk models applied to health data has been accepted at JMIR!
• Our time series causal discovery tutorial has been accepted at the IEEE Signal Processing Magazine! (forthcoming; preprint)
• Our work on causal actionable prediction models in psychosis is now out with BJPsych!
• Our paper on causal ordering for time series causal discovery has been accepted at TMLR!
• Workshop alert! Come to Edinburgh and discuss causality in healthcare with us. Join us!