Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third party sellers’ products to consumers. These rankings decrease consumer search costs and increase the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging since rankings are endogenous: highly ranked products are also the most relevant ones for consumers. In this dissertation, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to identify the causal effect of rankings. Using this data set, I make three contributions. First, I show that rankings affect what consumers search, but conditional on search, do not affect purchases. I also exploit a feature of the data set (opaque offers), to show that rankings lower search costs, instead of affecting consumer expectations or utility. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $2.64, lower than previous estimates in the literature obtained without experimental varia- tion. Finally, I show that a utility-based ranking built on this model’s estimates leads to an almost twofold increase in consumer welfare, while also increasing transactions and revenue for the search intermediary.