To infer the causal impact that a designed market intervention has exerted on an outcome metric over time has been a significant issue in econometrics and marketing. Last week, Google open sourced CausalImpact – a tool that will assist analysts in monitoring any measurable outcome with regards to products or policies, as a result of an designed intervention.
“Today (9th September), we’re excited to announce the release of CausalImpact, an open-source R package that makes causal analyses simple and fast. With its release, all of our advertisers and users will be able to use the same powerful methods for estimating causal effects that we’ve been using ourselves,” says the Google open source blog, announcing the launch.
“Our main motivation behind creating the package has been to find a better way of measuring the impact of ad campaigns on outcomes. However, the CausalImpact package could be used for many other applications involving causal inference,” Google said. “Examples include problems found in economics, epidemiology, or the political and social sciences.”
Google explains that, utilising a Bayesian framework to estimating the causal effect of a designed intervention on a time series, the guiding variables include a response time series (e.g., clicks) and a set of control time series (e.g., clicks in non-affected markets, clicks on other sites, or Google Trends data). These variables then establish a Bayesian structural time-series model with a built-in spike-and-slab prior for automatic variable selection. This model then predicts how the response metric would have evolved after the intervention if the intervention had not occurred.
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(Image credit: Google)