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YSPH Biostatistics Seminar: "Individual causal inference and time series methods for health studies with missing data"

NOTE: BIS 526 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

Charlotte Fowler, PhD candidate

Department of Biostatistics

Columbia University Mailman School of Public Health


Data from smartphones and wearable devices provide rich longitudinal information on participants and allow for causal inference for daily exposures and outcomes. However, informative missingness, latent variables, and unmeasured confounding are common in mobile health studies an introduce bias. In addition, there are likely violations to stationarity, a key assumption for traditional longitudinal methods. To overcome this challenge, we first propose an expectation maximization algorithm to adapt the conventional test for unit root non-stationarity to a context with missing data and develop a sensitivity analysis for data missing not at random. Using our method, we identify a patient with bipolar spectrum disorder who has a unit root in their daily negative mood score data. We hypothesize the non-stationarity may result from the underlying latent disease states such as mania or depression, and thus we additionally develop a model to identify and control for latent modification and confounding. Specifically, we propose a hidden Markov model for individual causal inference which handles missing data in the outcome through marginalization and multiple imputation. We compare the performance of our proposed model with a frequentist and a Bayesian implementation to a naive approach in a simulation and application to a multi-year smartphone study of bipolar patients. We employ the approach to evaluate the individual effect of digital social activity on self-reported loneliness across different latent disease states.


  • Columbia University Mailman School of Public Health

    Charlotte Fowler
    PhD candidate


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Lectures and Seminars
Jan 202416Tuesday