Jim Koehler

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.

Incremental Clicks: The Impact of Search Advertising

ABSTRACT In this research, the authors examined how the number of organic clicks changed when search ads were present and when search ad campaigns were turned off. The authors developed a statistical model to estimate the fraction of total clicks that could be attributed to search advertising. A meta-analysis of several hundred of these studies revealed that more than 89 percent of the ads clicks were incremental, in the sense that those visits to the advertisers site would not have occurred without the ad campaigns.

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.