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DTSTART;TZID=America/Los_Angeles:20251001T120000
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SUMMARY:Workshop: Kara Rudolph\, Columbia University\, Causal Mediation Workshop
DESCRIPTION:Biography: Kara Rudolph is an epidemiologist interested in developing and applying causal inference methods to better understand the prevention and treatment of substance use disorders. Currently\, she is a Associate Professor of Epidemiology at Columbia University. Her current work focuses on developing and applying methods for transportability and mediation to understand mechanisms relevant for drug use disorder prevention and treatment in various target populations. More generally\, her work on generalizing/ transporting findings from study samples to target populations and identifying subpopulations most likely to benefit from interventions contributes to efforts to optimally target available policy and program resources. She has completed a PhD in Epidemiology and an MHS in Biostatistics from the Johns Hopkins Bloomberg School of Public Health and was a Robert Wood Johnson Foundation Health and Society Scholar. \nCausal Mediation Workshop\nCausal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome\, a central goal in epidemiology and health sciences. However\, rapid methodologic developments coupled with few formal courses presents challenges to implementation. Beginning with an overview of classical direct and indirect effects\, this workshop will present recent advances that overcome limitations of previous methods\, allowing for: (i) continuous exposures\, (ii) multiple\, non-independent mediators\, and (iii) effects identifiable in the presence of intermediate confounders affected by exposure. Emphasis will be placed on flexible\, stochastic and interventional direct and indirect effects\, highlighting how these may be applied to answer substantive epidemiological questions from real-world studies. Multiply robust\, nonparametric estimators of these causal effects\, and free and open source R packages (crumble) for their application\, will be introduced. To aid translation to real-world data analysis\, this workshop will incorporate hands-on R programming exercises to allow participants practice in implementing the statistical tools presented. It is recommended that participants have working knowledge of the basic notions of causal inference\, including counterfactuals and identification (linking the causal effect to a parameter estimable from the observed data distribution). Familiarity with the R programming language is also recommended. \n  \nAn recording of Kara Rudolph’s presentation may be accessed here.
URL:https://ccpr.ucla.edu/event/kara-rudolph-columbia-university-workshop-tbd/
LOCATION:Room 4240A\, 4th Floor\, Public Affairs Building\, 337 Charles Young Dr.\, LA\, CA 90095
CATEGORIES:CCPR Workshop
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DTSTART;TZID=America/Los_Angeles:20251008T123000
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DTSTAMP:20260502T143328
CREATED:20250805T173634Z
LAST-MODIFIED:20250922T173842Z
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SUMMARY:Workshop: CCPR Computing and Data Orientation
DESCRIPTION:Our Computing Orientation provides an overview of the technological resources\, services\, and support available through CCPR to advance affiliates’ research. The session will cover data management planning and security requirements\, guidance on choosing and accessing appropriate computational resources (including individual\, centralized\, and high-performance environments)\, and an introduction to the Secure Data Enclave for projects with heightened security needs. Participants will also learn about available statistical consultation services\, recommended research tools\, and upcoming infrastructure projects.\n\n \nThis orientation is designed for CCPR affiliates seeking to understand the full range of computing resources offered by the center\, as well as best practices for accessing and leveraging them effectively in their research.
URL:https://ccpr.ucla.edu/event/workshop-ccpr-computing-orientation-2/
LOCATION:Room 4240A\, 4th Floor\, Public Affairs Building\, 337 Charles Young Dr.\, LA\, CA 90095
CATEGORIES:CCPR Workshop
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DTSTART;TZID=America/Los_Angeles:20251015T120000
DTEND;TZID=America/Los_Angeles:20251015T150000
DTSTAMP:20260502T143328
CREATED:20250805T175751Z
LAST-MODIFIED:20250805T175751Z
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SUMMARY:Workshop: Brandon Stewart\, Princeton University\, "Using Large Language Model Annotations for the Social Sciences: A General Framework of Using Predicted Variables in Statistical Analyses"
DESCRIPTION:Biography: Brandon Stewart is Associate Professor of Sociology at Princeton University where he is also affiliated with the Office of Population Research and numerous other centers on campus. He currently serves as the Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research. His work spans several areas of computational social science with a focus on text as data and causal inference. \n  \n\n\n\n“Using Large Language Model Annotations for the Social Sciences: A General Framework of Using Predicted Variables in Statistical Analyses”\n\n\n\nAbstract: Social scientists use automated annotation methods\, such as supervised machine learning and\, more recently\, large language models (LLMs)\, that can predict labels and generate text-based variables. While such predicted text-based variables are often analyzed as if they were observed without errors\, we show that ignoring prediction errors in the automated annotation step leads to substantial bias and invalid confidence intervals in downstream analyses\, even if the accuracy of the automated annotations is high\, e.g.\, above 90%. We propose a framework of design-based supervised learning (DSL) that can provide valid statistical estimates\, even when predicted variables contain non-random prediction errors. DSL employs a doubly robust procedure to combine predicted labels and a smaller number of expert annotations. DSL allows scholars to apply advances in LLMs to social science research while maintaining statistical validity. We illustrate its general applicability using two applications where the outcome and independent variables are text-based.
URL:https://ccpr.ucla.edu/event/workshop-brandon-stewart-princeton-university-using-large-language-model-annotations-for-the-social-sciences-a-general-framework-of-using-predicted-variables-in-statistical-analyses/
LOCATION:Room 4240A\, 4th Floor\, Public Affairs Building\, 337 Charles Young Dr.\, LA\, CA 90095
CATEGORIES:CCPR Workshop
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