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Disentangling Confounding and Nonsense Associations Due to Dependence

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

March 08, 2022 (08:00 AM PST - 08:25 AM PST)
Speaker(s): Betsy Ogburn (Johns Hopkins University)
Location: SLMath: Online/Virtual
Primary Mathematics Subject Classification No Primary AMS MSC
Secondary Mathematics Subject Classification No Secondary AMS MSC
Video

Disentangling Confounding And Nonsense Associations Due To Dependence

Abstract

Nonsense associations can arise when an exposure and an outcome of interest exhibit similar patterns of dependence. Confounding is present when potential outcomes are not independent of treatment. This talk will describe how confusion about these two phenomena results in shortcomings in popular methods in three areas: causal inference with multiple treatments and unmeasured confounding, causal and statistical inference with social network data, and spatial confounding. For each of these areas I will demonstrate the flaws in existing methods and describe new methods that were inspired by careful consideration of dependence and confounding.

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Disentangling Confounding And Nonsense Associations Due To Dependence

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