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DTSTART;TZID=America/New_York:20240912T160000
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SUMMARY:Hierarchical Causal Models
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 12\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 301 \nDavid Blei\, PhD\nProfessor of Statistics and Computer Science\nColumbia University \nAnalyzing nested data with hierarchical models is a staple of Bayesian statistics\, but causal modeling remains largely focused on “flat” models. In this talk\, we will explore how to think about nested data in causal models\, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data—replacing a flat causal model with a hierarchical causal model—can provide new opportunities for identification and estimation. \nAs examples\, we will study how to identify and estimate causal effects under unmeasured confounders\, interference\, and instruments. \nThis is joint work with Eli Weinstein.
URL:https://ds.dfci.harvard.edu/event/hierarchical-causal-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/blei_headshot_crop.jpg
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DTSTART;TZID=America/New_York:20240919T160000
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SUMMARY:Inference for Treatment-Specific Survival Curves using Machine Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 19\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 313 \nTed Westling\, Assistant Professor\, Department of Mathematics & Statistics\, University of Massachusetts Amherst \nIn the absence of data from a randomized trial\, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context\, interest often focuses on the treatment-specific survival curves; that is\, the survival curves were the entire population under study to be assigned to receive the treatment or not. Under certain causal conditions\, including that all confounders of the treatment-outcome relationship are observed\, the treatment-specific survival can be identified with a covariate-adjusted survival function. Several estimators of this function have been proposed\, including estimators based on outcome regression\, inverse probability weighting\, and doubly robust estimators. We propose a cross-fitted doubly-robust estimator that incorporates data-adaptive estimators of the conditional survival functions. We establish conditions on the nuisance estimators under which our estimator is consistent and asymptotically linear\, both pointwise and uniformly in time. We also propose an ensemble learner for combining multiple candidate estimators of the conditional survival estimators. Our methods and results accommodate events occurring in discrete or continuous time (or both). We investigate the practical performance of our methods using an application to the effect of a surgical treatment to prevent metastases of parotid carcinoma on mortality. Time permitting\, we will discuss ongoing work concerning sensitivity analysis for survival curves.
URL:https://ds.dfci.harvard.edu/event/inference-for-treatment-specific-survival-curves-using-machine-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/Ted-Westling-long.png
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