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- Publisher Website: 10.1145/3580507.3597701
- Scopus: eid_2-s2.0-85168162798
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Conference Paper: Estimating Effects of Long-Term Treatments
Title | Estimating Effects of Long-Term Treatments |
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Authors | |
Keywords | A/B tests digital platforms long-term treatments surrogates |
Issue Date | 12-Jul-2023 |
Publisher | Association for Computing Machinery, Inc |
Abstract | Randomized controlled trials (RCTs), also known as A/B tests, have become the gold standard for evaluating the effectiveness of product changes on digital platforms. Accurately estimating the effects of long-term treatments still remains a challenge. Product updates such as new user interfaces or recommendation algorithms are intended to persist in the system for an extended period. However, A/B testing is typically conducted for short durations, often less than two weeks, to facilitate rapid product iterations. Conducting lengthy experiments to capture the long-term impact of product changes becomes impractical due to potential negative impacts on user experiences, high opportunity costs associated with user traffic, and delays in decision-making processes. |
Persistent Identifier | http://hdl.handle.net/10722/338870 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Huang, S | - |
dc.contributor.author | Wang, C | - |
dc.contributor.author | Yuan, Y | - |
dc.contributor.author | Zhao, J | - |
dc.contributor.author | Zhang, J | - |
dc.date.accessioned | 2024-03-11T10:32:09Z | - |
dc.date.available | 2024-03-11T10:32:09Z | - |
dc.date.issued | 2023-07-12 | - |
dc.identifier.isbn | 9798400701047 | - |
dc.identifier.uri | http://hdl.handle.net/10722/338870 | - |
dc.description.abstract | <p>Randomized controlled trials (RCTs), also known as A/B tests, have become the gold standard for evaluating the effectiveness of product changes on digital platforms. Accurately estimating the effects of long-term treatments still remains a challenge. Product updates such as new user interfaces or recommendation algorithms are intended to persist in the system for an extended period. However, A/B testing is typically conducted for short durations, often less than two weeks, to facilitate rapid product iterations. Conducting lengthy experiments to capture the long-term impact of product changes becomes impractical due to potential negative impacts on user experiences, high opportunity costs associated with user traffic, and delays in decision-making processes.</p> | - |
dc.language | eng | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.relation.ispartof | 24th ACM Conference on Economics and Computation (09/07/2023-12/07/2023, London) | - |
dc.subject | A/B tests | - |
dc.subject | digital platforms | - |
dc.subject | long-term treatments | - |
dc.subject | surrogates | - |
dc.title | Estimating Effects of Long-Term Treatments | - |
dc.type | Conference_Paper | - |
dc.identifier.doi | 10.1145/3580507.3597701 | - |
dc.identifier.scopus | eid_2-s2.0-85168162798 | - |
dc.identifier.spage | 907 | - |