How many incremental clicks or conversions did an advertising campaign generate? Did the latest feature release increase customer satisfaction and revenue? Did an appearance in the news bring in new users? Only causal relationships empower us to understand the consequences of our actions and decide what to do next. This is why identifying causal effects has been at the heart of data science.
The gold standard for estimating a causal effect is a randomized experiment. However, clean experiments can be infeasible, too costly, or unethical. Observational analysis methods offer a powerful way out. Kay Brodersen offers an introduction to CausalImpact, a new analysis library developed at Google for identifying the causal effect of an intervention on a metric over time. The breadth of questions to which CausalImpact has been applied across science and industry since its release as open source software are amazing. Join Kay to learn how to estimate a causal effect in three lines of code and to hear about the lessons Google has learned from its own analyses.
Kay H. Brodersen is a data scientist at Google, where he works on Bayesian statistical models for causal inference in large-scale randomized experiments and anomaly detection in time series data. Kay studied at Muenster (Germany), Cambridge (UK), and Oxford (UK) and holds a PhD degree from ETH Zurich.
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