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Problem definition: Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Academic/practical relevance: Increasing match rates is a prevalent objective of online platforms. We provide insights into how to leverage users’ preferences and behavior toward this end. Our proposed algorithm was piloted by our collaborator, a major online dating company in the United States. Methodology: Our work combines several methodologies. We model the platform’s problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company’s algorithm in order to estimate the users’ preferences and the causal …