Running Experiments When There Are Network Effects Linear Digressions podcast

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Running experiments when there are network effects

6y ago 24:45

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Content provided by Ben Jaffe and Katie Malone, Ben Jaffe, and Katie Malone. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Ben Jaffe and Katie Malone, Ben Jaffe, and Katie Malone or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But that’s not a safe assumption, especially when there are network effects (like in almost any social context, for instance!) SUTVA, or the stable treatment unit value assumption, is a big phrase for this assumption and violations of SUTVA make for some pretty interesting experiment designs. From news feeds in LinkedIn to disentangling herd immunity from individual immunity in vaccine studies, indirect (i.e. network) effects in experiments can be just as big as, or even bigger than, direct (i.e. individual effects). And this is what we talk about this week on the podcast. Relevant links: http://hanj.cs.illinois.edu/pdf/www15_hgui.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2600548/pdf/nihms-73860.pdf

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293 episodes

#Data Science #Science #Data #Linear #Digress #Ben Jaffe and Katie Malone #Ben Jaffe #Katie Malone #Tech #Machining #Learn

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Running experiments when there are network effects

Linear Digressions

1,448 subscribers

published 6y ago

Share

MP3•Episode home

Content provided by Ben Jaffe and Katie Malone, Ben Jaffe, and Katie Malone. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Ben Jaffe and Katie Malone, Ben Jaffe, and Katie Malone or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But that’s not a safe assumption, especially when there are network effects (like in almost any social context, for instance!) SUTVA, or the stable treatment unit value assumption, is a big phrase for this assumption and violations of SUTVA make for some pretty interesting experiment designs. From news feeds in LinkedIn to disentangling herd immunity from individual immunity in vaccine studies, indirect (i.e. network) effects in experiments can be just as big as, or even bigger than, direct (i.e. individual effects). And this is what we talk about this week on the podcast. Relevant links: http://hanj.cs.illinois.edu/pdf/www15_hgui.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2600548/pdf/nihms-73860.pdf

… continue reading

293 episodes

#Data Science #Science #Data #Linear #Digress #Ben Jaffe and Katie Malone #Ben Jaffe #Katie Malone #Tech #Machining #Learn

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