gov3009-l November 2019

gov3009-l@lists.fas.harvard.edu

1 participants
3 discussions

Gov 3009 Applied Stats Workshop (11/20) - Lucas Janson

by Evans, Georgina

Dear Workshop Community, Our next meeting will be *Wednesday November 20*, where Lucas Janson will present research on* "*Recent Advances in Model-X Knockoffs*"* *Abstract*: Two years ago in this workshop I presented my work on model-X knockoffs, a method for high-dimensional variable selection that provides exact (finite-sample) control of false discoveries and high power as a result of its flexibility to leverage any and all domain knowledge and tools from machine learning to search for signal. In this talk, I will discuss two recent works that significantly advance the usability and generality of model-X knockoffs. First, I will show how the original assumptions of model-X knockoffs, that the multivariate distribution of the covariates be known exactly, can be significantly relaxed to the assumption that only a *model* for the covariates be known, and that model can have as many free parameters as the *product* of the sample size and dimension. No loss in the guarantees of knockoffs is incurred by this relaxation of the assumptions. Second, I will show how to efficiently and exactly sample knockoffs for *any *distribution on the covariates, even if the distribution is only known up to a normalization constant. This dramatically expands the set of covariate distribution for which we can apply knockoffs. This is joint work with a number of collaborators, listed below in the full references for the two works: *D. Huang and L. Janson. Relaxing the Assumptions of Knockoffs by Conditioning. Annals of Statistics (to appear), 2019.* *S. Bates, E. Candès, L. Janson, and W. Wang. Metropolized Knockoff Sampling. 2019.* *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 20 at 12 noon - 1:30 pm. All are welcome! Lunch will be provided. Best, Georgie

4 years, 5 months

Gov 3009 Applied Stats Workshop (11/13) - Xiao-Li Meng

by Evans, Georgina

Dear Workshop Community, Our next meeting will be *Wednesday November 13*, where Professor Xiao-Li Meng will present research on* "2020 Election and Privacy Protected Census: Data Quantity vs. Quality & Privacy vs. Utility"* *Abstract:* The year 2020 will be a busy one for statisticians and more generally for data scientists; predictions about the 2020 US election are already underway. Will the lessons from the 2016 US election be learned, or will the prediction failure be repeated? How do we measure the quality of the data we rely upon for predictions? How small are our big data when we take their quality into account? The US Census Bureau has announced that the data from the 2020 Census will be released under differential privacy protection, which – in layperson’s terms – means adding some noise to the data in order to prevent re-identification of individuals and other privacy-related threats. Few would argue against protecting data privacy, but what trade-offs would be acceptable between data privacy and data utility? How much information do we lose by making data differentially private? How should we analyze differential privacy protected data? This talk invites the audience on a journey of deep statistical thinking prompted by these questions, regardless of whether or not they have any interest in the US politics and census. *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 13 at 12 noon - 1:30 pm. All are welcome! Lunch will be provided. Best, Georgie *Many of you will be interested in the second issue of the Harvard Data Science Review, which Professor Meng helped launch. You can find it here: https://hdsr.mitpress.mit.edu <https://hdsr.mitpress.mit.edu>.*

4 years, 5 months

Gov 3009 Applied Statistics Workshop (11/06) - Nicole Pashley

by Evans, Georgina

Dear Workshop Community, Our next meeting will be *Wednesday November 6*, where Nicole Pashley will present research on* "Causal Inference for Multiple Non-Randomized Treatment Using Fractional Factorial Designs"* *Abstract:* We explore a framework for addressing causal questions in an observational setting with multiple treatments. This setting involves attempting to approximate an experiment from observational data. With multiple treatments, this experiment would be a factorial design. However, certain treatment combinations may be so rare that, for some combinations, we have no measured outcomes in the observed data. We propose to conceptualize a hypothetical fractional factorial experiment instead of a full factorial experiment and lay out a framework for analysis in this setting. We also connect our design-based methods to standard regression methods. We illustrate the method and the challenges of this type of data through application. *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, November 6 at 12 noon - 1:30 pm. All are welcome. Lunch will be provided. Best, Georgie

4 years, 5 months

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gov3009-l November 2019