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[IISA Webinar] Statistical Inference for Treatment Effects and Generalized Linear Models under High-Dimensional Proportional Asymptotics

  • 1.  [IISA Webinar] Statistical Inference for Treatment Effects and Generalized Linear Models under High-Dimensional Proportional Asymptotics

    Posted 5 days ago

    The Webinar Committee of the International Indian Statistical Association (IISA) proudly present a webinar by Dr. Rajarshi Mukherjee (Associate Professor, Department of Biostatistics, Harvard University): โ€œStatistical Inference for Treatment Effects and Generalized Linear Models under High-Dimensional Proportional Asymptoticsโ€.

    ๐Ÿ—“ Date: Thursday, March 20, 2025

    โฐ Time: 9:30 - 10:30 AM EST

    ๐ŸŒ Location: Virtual (via Zoom)

    Abstract: In this talk we will discuss statistical inference of average treatment effect in measured confounder settings as well as parallel questions of inferring linear and quadratic functionals in generalized linear models under high dimensional proportional asymptotic settings i.e. when ๐‘๐‘›โ†’๐›ฟโˆˆ(0,โˆž) where ๐‘,๐‘› denote the dimension of the covariates and the sample size respectively . The results rely on the knowledge of the variance covariance matrix ฮฃ of the covariates under study and we show that whereas โˆš๐‘›-consistent asymptotically normal inference is possible for any ๐›ฟ by using method of moments type estimators that do not rely on estimating high dimensional nuisance parameters followed by a debiasing strategy. Without the knowledge of ฮฃ we first develop โˆš๐‘›-consistent estimators by using simple estimators of ฮฃ when ๐›ฟ<1. Subsequently for ๐›ฟโ‰ฅ1 we develop consistent estimators of the quantities of interest and argue that โˆš๐‘›-consistent estimation might not be possible without further assumptions on ฮฃ. Finally we verify our results in numerical simulations. This talk is based on joint work with Xingyu Chen and Lin Liu from Shanghai Jiao Tong University.

    Speaker Bio: Rajarshi Mukherjee is an Associate Professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Before joining Harvard, he was an Assistant Professor in the Division of Biostatistics at UC Berkeley, following his tenure as a Stein Fellow in the Department of Statistics at Stanford University. He earned his PhD in Biostatistics from Harvard University under the supervision of Professor Xihong Lin. Prior to his doctoral studies, he completed his B.Stat (Hons) and M.Stat, specializing in Mathematical Statistics and Probability, at the Indian Statistical Institute, Kolkata.Dr. Mukherjee's research focuses on causal inference in observational studies within modern data settings, particularly in addressing statistical challenges related to environmental mixtures and their impact on cognitive development in children and cognitive decline in aging populations. His work is also driven by applications in large-scale genetic association studies, statistical methods for assessing the health effects of climate change, and understanding the impact of homelessness on human health.


    Registration (free but required):     https://weillcornell.zoom.us/webinar/register/WN_G5GwUU06Thq2v3tstE2Dtg 

    A flyer is attached to this post, and available for download at this link: https://www.dropbox.com/scl/fi/3zyl6fol3pfra7wi0dbgx/IISA-Flyer-March-2025.pdf?rlkey=47l86p5ui5ryeztbciwacpvh9&st=62vlgdoh&dl=0



    ------------------------------
    Himel Mallick, PhD, FASA
    Principal Investigator (Tenure-track Faculty)
    Cornell University
    New York, New York 10065
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