PhD Position in Causal Inference & Machine Learning

• Progress in statistics and machine learning research has led to great strides in using data to uncover relationships between things in the world we observe around us. Many questions of scientific and societal (e.g. medical) importance, however, are causal questions. Causal questions ask what the effects of new actions will be, rather than how things are related in a world in which we keep things as is.   
• Given the importance of answering such causal questions, many data-driven methods have been developed to estimate causal effects in machine learning as well as statistics, epidemiology, econometrics, psychometrics, and many others. However, these methods all rely on causal assumptions, such as positivity and unconfoundedness. When such assumptions are violated, these methods will return incorrect causal answers, which leads to flawed and potentially even harmful decision-making. Currently, however, there is a lack of data-driven methods to detect assumption violations, leading to problems that go undetected, and ultimately, untrustworthy causal claims.   
• In this project, our goal is to better understand the limits of the detection of causal assumption violations, develop new detection methods, incorporate these violations into causal models, and design procedures to mitigate their influence. By offering these tools to practitioners, we will contribute to a new, safer approach to addressing causal questions that leads to more trustworthy causal answers.   
• The project particularly focusses on positivity violations, that is the assumption that it is possible for each treatment of interest to have occurred for each relevant unit in the data. We are interested in developing methods to detect whether this assumption is violated, to find out for which treatments or subpopulations it holds, and to properly account for the uncertainty caused by limited support in the data. We want to investigate this in both simple and more complicated (e.g. high-dimensional or longitudinal) settings.   
• The project is part of the "Safe Causal Inference" consortium, a multi-disciplinary (computer science, mathematics, biostatistics, epidemiology) consortium spanning eight PhD positions to improve the trustworthiness of causal methods. Through the consortium, you will be able to collaborate with experts and peers from multiple causal inference disciplines to strengthen and inspire your work.   
• We are looking for an enthusiastic and self-motivated person with a passion for research. We offer a stimulating scientific research environment and the embedding in a multi-disciplinary consortium that allows you to develop skills that go beyond discipline-specific boundaries. You will be supervised by Jesse Krijthe (computer science, TU Delft) and Nan van Geloven (biostatistics, Leiden University Medical Center) and be primarily based in the Pattern Recognition and Bioinformatics group of TU Delft, which includes researchers working on the methodology of machine learning, bioinformatics, computer vision, and socially perceptive computing.