Research talk: Causal ML and fairness
504
28
Microsoft Research334 тыс
Следующее
08.02.22 – 47416:53Популярные
53 дня – 1 6012:05
Опубликовано 8 февраля 2022, 17:17
Speaker: Allison Koenecke, Postdoc, Microsoft Research New England
Observing heterogeneous treatment effects across different demographic groups is an important mechanism for evaluating fairness. However, relatively little data is available for certain demographics, in which case researchers may combine multiple data sources to increase statistical power. The stakes are especially high in healthcare—it is imperative to accurately measure the effectiveness of treatments for diseases that could disproportionately impact underrepresented patient subgroups. Join researcher Allison Koenecke, from the Machine Learning & Statistics Group at Microsoft Research New England, to discuss federated causal inference. Because legal and privacy considerations may restrict individual-level information sharing across data sets, we introduce federated methods for treatment effect estimation that only utilize summary-level statistics from each data set. These asymptotically guaranteed methods provide variance estimates and doubly robust treatment effects under model assumptions on heterogeneous data sets.
Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit
Observing heterogeneous treatment effects across different demographic groups is an important mechanism for evaluating fairness. However, relatively little data is available for certain demographics, in which case researchers may combine multiple data sources to increase statistical power. The stakes are especially high in healthcare—it is imperative to accurately measure the effectiveness of treatments for diseases that could disproportionately impact underrepresented patient subgroups. Join researcher Allison Koenecke, from the Machine Learning & Statistics Group at Microsoft Research New England, to discuss federated causal inference. Because legal and privacy considerations may restrict individual-level information sharing across data sets, we introduce federated methods for treatment effect estimation that only utilize summary-level statistics from each data set. These asymptotically guaranteed methods provide variance estimates and doubly robust treatment effects under model assumptions on heterogeneous data sets.
Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit
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