Google Tool Infers Cause from Correlations

 Software  

Google has announced an open source package for the R statistical computing software designed to help users infer whether a particular action really did cause subsequent activity. The tool, called CausalImpact, was initially created to measure AdWords campaigns but it has potential for much broader uses.

Google has announced a new open source tool that can help data analysts decide if changes to products or policies resulted in measurable change, or if the change would have happened anyway. The tool, called CausalImpact, is a package for the R statistical computing software.

The package uses a structural Bayesian time-series model to estimate how the response metric would have evolved after the intervention if the intervention had not occurred.

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Google created CausalImpact primarily for quantifying the effectiveness of AdWords campaigns.

"The CausalImpact package could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, or the political and social sciences."

Google developer Kay Brodersen describes how CausalImpact works. "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. Examples include problems found in economics, epidemiology, or the political and social sciences."

The blog post has a more in depth explanation of the package, as well as instructions for installing it via GitHub.

The differences between causation and correlation and the importance of not conflating the two when dealing with big data is important. Although all of those concerns hold true, especially if when using data to solve a problem or to inform policy strategies that could have meaningful negative effects on individuals, this type of tool is still potentially very useful.

Strong causal inference could serve as a jumping-off point for a deeper study of cause and effect, and for applications such as advertising, marketing or site/app design it might be good enough.

With executives, companies, politicians and others voice the importance of data-driven decision-making, Google's open source tool may be very helpful.


SOURCE  GigaOM

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