Inference and Interference in Marketplace Experimentation

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=I8RjidAyuns


Duration: 45:15
441 views
11

Hannah Li (Stanford)
https://simons.berkeley.edu/talks/tbd-469
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

Online marketplace platforms rely on experiments to aid decision-making. Due to interference effects, however, common estimators may be biased in these market settings. Prior work has focused on the biases that arise in the treatment effect estimates, but there is also bias in the typical standard error estimates, which can cause the platform to be under or over-confident in its estimates. In this work we study the standard error bias and its impact on the resulting platform decisions. We utilize a dynamic market model which captures the marketplace interference effects. We show that commonly used standard error estimators are biased in market settings. We explore practical methods to reduce the standard error bias. Finally, using calibrations to real marketplace data, we assess the quality of the ultimate decisions made using these biased estimates.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Quantifying Uncertainty: Stochastic Adversarial and Beyond
Hannah Li