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Activity Number: 313
Type: Invited
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Marketing
Abstract #310654
Title: What Did We Accomplish? Inferring the Causal Effect of a Market Intervention by Counterfactual Forecasting
Author(s): Kay H. Brodersen*+ and Fabian Gallusser and Jim Koehler and Nicolas Remy and Steven L. Scott
Companies: Google and Google and Google and Google and Google
Keywords: causal inference ; forecasting ; Bayesian inference ; time series ; marketing ; experimental
Abstract:

Inferring the causal effect of a market intervention (e.g., an advertising campaign) is difficult, especially when a randomized experiment was not possible. In this work, we introduce Bayesian structural time-series models that use synthetic controls (e.g., unaffected markets) to infer the temporal evolution of causal impact attributable to the intervention. Such models can be used, for example, to show how a marketing effect unfolded over time or assess whether it justified the underlying spending. Inference is based on an efficient MCMC implementation that enables near-interactive analyses. Our approach is being used in an increasing number of applications at Google, and we anticipate that it will prove useful in many analysis efforts elsewhere.


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