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Measuring the causal effects of online advertising (adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experiments, we show that observational data frequently lead to incorrect estimates of adfx. The reason, which we label "activity bias," comes from the surprising amount of time-based(More)
"North" ads, or sponsored listings appearing just above the organic search results, generate the majority of clicks and revenues for search engines. In this paper, we ask whether the competing north ads exert externalities on each other. In particular, does increasing the number of rival north ads decrease the number of clicks I receive on my own north ad?(More)
We study the impact of display advertising on user search behavior using a field experiment. In such an experiment, the treatment group users are exposed to some display advertising campaign, while the control group users are not. During the campaign and the post-campaign period we monitor the user search queries and we label them as relevant or irrelevant(More)
Online consumer data presents new opportunities for measuring the effects of advertising. We combine search query data with the television commercial schedule for the 2011 Super Bowl to measure the causal impact of TV advertising on consumer search behavior. Examining 46 Super Bowl commercials, we generally find large spikes in search behavior related to(More)
(2016). Investigating dark matter substructure with pulsar timing-I. Constraints on ultracompact minihaloes. Monthly Notices of the Royal Astronomical Society, 456(2). <a href=" Comparing the observable properties of dwarf galaxies on and off the andromeda plane. (2015). Investigating dark matter substructure with pulsar timing-II. Improved limits on(More)
Monte Carlo Sampling was used to generate a set of uniform, feasible flux distributions (points). The method is based on the Artificially Centered Hit and Run (ACHR) algorithm with slight modifications 1. Initially a set of non-uniform pseudo-random points, called warm-up points, is generated. In a series of iterations, each point is randomly moved, always(More)
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