2 research outputs found
Causality and Robust Optimization
A decision-maker must consider cofounding bias when attempting to apply
machine learning prediction, and, while feature selection is widely recognized
as important process in data-analysis, it could cause cofounding bias. A causal
Bayesian network is a standard tool for describing causal relationships, and if
relationships are known, then adjustment criteria can determine with which
features cofounding bias disappears. A standard modification would thus utilize
causal discovery algorithms for preventing cofounding bias in feature
selection. Causal discovery algorithms, however, essentially rely on the
faithfulness assumption, which turn out to be easily violated in practical
feature selection settings. In this paper, we propose a meta-algorithm that can
remedy existing feature selection algorithms in terms of cofounding bias. Our
algorithm is induced from a novel adjustment criterion that requires rather
than faithfulness, an assumption which can be induced from another well-known
assumption of the causal sufficiency. We further prove that the features added
through our modification convert cofounding bias into prediction variance. With
the aid of existing robust optimization technologies that regularize risky
strategies with high variance, then, we are able to successfully improve the
throughput performance of decision-making optimization, as is shown in our
experimental results
Revenue Maximization of Airbnb Marketplace using Search Results
Correctly pricing products or services in an online marketplace presents a
challenging problem and one of the critical factors for the success of the
business. When users are looking to buy an item they typically search for it.
Query relevance models are used at this stage to retrieve and rank the items on
the search page from most relevant to least relevant. The presented items are
naturally "competing" against each other for user purchases. We provide a
practical two-stage model to price this set of retrieved items for which
distributions of their values are learned. The initial output of the pricing
strategy is a price vector for the top displayed items in one search event. We
later aggregate these results over searches to provide the supplier with the
optimal price for each item. We applied our solution to large-scale search data
obtained from Airbnb Experiences marketplace. Offline evaluation results show
that our strategy improves upon baseline pricing strategies on key metrics by
at least +20% in terms of booking regret and +55% in terms of revenue
potential