Google Boosts Open Source R

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gooby Jelani Harper

A recent article from Derrick Harris states: “Google announced on Tuesday 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, and Google details it in a blog post. According to blog post author Kay H. Brodersen, Google uses the tool — created it, in fact — primarily for quantifying the effectiveness of AdWords campaigns. However, he noted, the same method could be used to gauge everything from whether adding a new feature caused an increase in app downloads to questions involving events in medical, social or political science.”

The author added: “The differences between causation and correlation — and the importance of not conflating the two just because you’re now dealing with big data — has been explained ad nauseam. And although all of those concerns hold true, especially if we’re 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. At any rate, as companies like Google keep touting the importance of data-driven decision-making, it’s good to see them help out the cause by releasing some tools that will make it easier for folks to do just that.”

Read more here.

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