{"id":11831,"date":"2020-11-06T07:07:23","date_gmt":"2020-11-06T12:07:23","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=11831"},"modified":"2021-04-21T07:57:46","modified_gmt":"2021-04-21T11:57:46","slug":"switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash\/11831\/","title":{"rendered":"Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash"},"content":{"rendered":"Originally published in DoorDash Engineering Feb 13, 2018. To A\/B or not to A\/B, that is the question Overview On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A\/B tests are often ineffective due to network effects. To be able to experiment in the face of network effects, we use a technique known as switchback testing, where we switch back and forth between treatment and control in particular regions over time. This approach resembles A\/B tests in <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash\/11831\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Originally published in DoorDash Engineering Feb 13, 2018. To A\/B or not to A\/B, that is the question Overview On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A\/B tests are often ineffective due [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[1097,59,230,1143,243,8,1144],"class_list":["post-11831","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand","tag-algorithms","tag-analytics","tag-data-analytics","tag-doordash","tag-machine-learning","tag-predictive-analytics","tag-supply-analytics"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/11831","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=11831"}],"version-history":[{"count":3,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/11831\/revisions"}],"predecessor-version":[{"id":12090,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/11831\/revisions\/12090"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=11831"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=11831"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=11831"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}