Nicolas Remy

Inferring causal impact using Bayesian structural time-series models

An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must determine the incremental contributions that dierent advertising campaigns have made to web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diusion-regressi on state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In con- trast to classical dierence-in-dier ences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) exibly accommodate multiple sources of variation, including the time-varying inuence of contemporane- ous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the eect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of our approach in improving the accuracy of causal at- tribution, power analyses, and principled budget allocation.

Collaboration in the cloud at Google

Through a detailed analysis of activity for all Google employees, this study shows how the Google Docs suite (documents, spreadsheets and slides) enables and increases collaboration within Google. In particular, visualization and analysis of the evolution of Googles collaboration network show that new employees have started collaborating more quickly and with more people as usage of Docs has grown. Over the last two years, the percentage of new employees who collaborate on Docs per month has risen from 70% to 90%, and the percentage who collaborate with more than two people has doubled from 35% to 70%. Moreover, the culture of collaboration has become more open, with public sharing within Google overtaking private sharing.

When to Combine Television With Online Campaigns: Cost Savings Versus Extended Reach

ABSTRACT This work investigates under what circumstances a television campaign should be complemented with online advertising to increase combined reach. The authors first proposed probabilistic models to derive necessary and sufficient optimality conditions for the best media mix. They then relied on roughly 26,000 television campaigns to train classification models to decide whether a campaign should add online advertising. Linear and support vector regression models are used to predict optimal budget allocation, cost savings, and additional reach. The resulting meta-study yields simple, interpretable, and actionable rules that improve the understanding of media-mix advertising.