Complexity Postdoctoral Fellow
David’s research focuses mostly on mathematical models that represent the causal structure of their target systems. These models use techniques from probability theory and graph theory to help scientists predict the outcomes of hypothetical interventions and experiments. Graphical causal models are important to philosophers of science because they help formalize at least two notions that have long resisted definitive philosophical analysis: causation and explanation.
In his PhD work, David focused on the problem of granularity in causal models, asking the question: how should scientists choose variables for their causal models at an appropriate level of grain? He uses techniques from decision theory to provide a precise answer to this question, albeit one that is inherently context-dependent. In future work, David plans to investigate the scientific and philosophical potential of algorithmic causal models, which formalize causal structure in terms of computational complexity rather than probabilistic independence. He also plans to apply these techniques to case studies in the natural and social sciences.
David has a PhD in Philosophy from the London School of Economics and Political Science (LSE). He holds an MSc in Philosophy and Public Policy from LSE, and a bachelor’s degree in Philosophy from Dartmouth College.