Practical Algorithms for Modeling Causality to Accelerate Scientific Discovery

Abstract

Scientific research revolves around the discovery and validation of causal relationships between variables. Machine learning has the potential to increase the efficiency of this process by proposing novel hypotheses from data observations, or by designing experiments that maximize success rate. This thesis addresses these problems through pragmatic approaches, designed to model large systems and incorporate rich domain knowledge. These algorithms are applied to use cases in molecular biology and drug discovery, which highlight their potential to inform efficient experiment design and to automate the analysis of experimental results.