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X-WR-CALNAME;VALUE=TEXT:Statistics Colloquium: Jelena Bradic (University of California San Diego)
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SUMMARY:Statistics Colloquium: Jelena Bradic (University of California San Diego)
DESCRIPTION:<h3>	<drupal-media data-entity-type="media" data-entity-uuid="9f989c28-661a-4c43-a741-021f5b321c2a" data-align="left" alt="Headshot of Jelena Bradic" data-view-mode="hwp_small"></drupal-media><u>Title: </u></h3><p>	<span>Dynamic Causal Learning: excursions in double robustness</span></p><h3>	<u>Abstract:</u></h3><p>	<span><span style="color:black">Recent progress in machine learning provides many potentially effective tools to learn estimates or make predictions from datasets of ever-increasing sizes. Can we trust such tools in clinical settings? If a learning algorithm predicts an effect of a new policy to be positive, what guarantees do we have concerning the accuracy of such prediction? The talk introduces new statistical ideas to ensure that the learned estimates in dynamic treatment settings satisfy some fundamental properties. The talk will discuss potential connections and departures between causality and robustness.</span></span></p>
LOCATION:Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
STATUS:CONFIRMED
DTSTART:20211025T190000Z
DTEND:20211025T200000Z
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