A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
Reducing in- terference bias in online marketplace pricing experiments.arXiv preprint arXiv:2004.12489, 2020
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Airbnb researchers use causal inference on guest booking data to estimate price sensitivity and heterogeneity for optimizing host pricing tools and guest matching.
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Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
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Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example
Airbnb researchers use causal inference on guest booking data to estimate price sensitivity and heterogeneity for optimizing host pricing tools and guest matching.