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  • PhD Dissertation Defense - Myrl Marmarelis

    Tue, May 28, 2024 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Robust Causal Inference with Machine Learning on Observational Data
    Date and Time: Tuesday, May 28th - 2:00pm - 4:00pm
    Committee: Aram Galstyan (Chair), Greg Ver Steeg, Fred Morstatter, Shanghua Teng, and Roger Ghanem (external)
    The rise of artificial intelligence and deep learning has led to unprecedented capabilities in prediction. As these black-box algorithms are deployed in different parts of society, it is becoming increasingly clear that predictions alone do not always translate to enabling effective decisions, policies, or reliable forecasts in a changing world. What is often needed is a stronger understanding of a system than a predictive model of observations can offer. This deficit arises when attempting to predict the system’s behavior in novel situations. Causal inference refers to a set of theoretical frameworks and practical methods for identifying cause-and-effect structures from data. Knowledge of this structure can help anticipate what would happen in a novel situation, like subjecting the system to intervention. Much work in causal inference is concerned with finding the minimal assumptions required to answer specific causal questions, like estimating the effect of a certain treatment. The more reasonable and relaxed the assumptions of a causal-inference method, the more applicable it is to diverse datasets and machine learning. There are many methodological aspects to performing causal inference on observational data—that is, without the ability to perform experiments. Of fundamental significance is having workable representations of the system that can be learned from data. Closely related to the quality of the representations is the ability to make downstream causal estimates robust to confounding. Confounders in a system are common structures that might confuse apparent relations between cause and effect, or treatment and outcome.
    In this dissertation, I propose methods for addressing these problems in challenging machine-learning contexts. I introduce an improved representation of single-cell RNA sequencing data for inference tasks in medicine and biology. Looking for high-dimensional interactions in biological processes leads to better resolution of phenotypes. More broadly, I make numerous contributions towards increased robustness of machine learning to hidden or observed confounding. I address sensitivity of dose-response curves to hidden confounding, prediction of interventional outcomes under hidden confounding; robust effect estimation for continuous-valued and multivariate interventions, and estimation for interventions that might only encourage treatment as a function of susceptibility.

    Location: Information Science Institute (ISI) - 553

    Audiences: Everyone Is Invited

    Contact: Myrl Marmarelis

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