Detecting Interference in Online Controlled Experiments with Increasing Allocation
Author:
Affiliation:
1. Stanford University, Stanford, CA, USA
2. Harvard University, Cambridge, MA, USA
3. LinkedIn Corporation, Sunnyvale, CA, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3580305.3599308
Reference58 articles.
1. Joshua D Angrist and Jörn-Steffen Pischke . 2009. Mostly harmless econometrics: An empiricist's companion . Princeton university press . Joshua D Angrist and Jörn-Steffen Pischke. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton university press.
2. A General Method for Detecting Interference Between Units in Randomized Experiments
3. Estimating average causal effects under general interference, with application to a social network experiment
4. Exact p-Values for Network Interference
5. Patrick Bajari , Brian Burdick , Guido W Imbens , Lorenzo Masoero , James McQueen , Thomas Richardson , and Ido M Rosen . 2021. Multiple randomization designs. arXiv preprint arXiv:2112.13495 ( 2021 ). Patrick Bajari, Brian Burdick, Guido W Imbens, Lorenzo Masoero, James McQueen, Thomas Richardson, and Ido M Rosen. 2021. Multiple randomization designs. arXiv preprint arXiv:2112.13495 (2021).
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