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11.5 Actionable Insights

In this chapter, here are the actionable insights:

  • Design is key for causal inference. Validation comes from design, not from results.

  • Regression discontinuity is a design that can be used to operationalize many different types of randomness that occur naturally in web products; it’s a creative design technique, rather just relying on a break or level change in the treatment variable.

  • Understanding time-series data is key to modeling some of the best RD/natural experiments where treatment is a function of time.

  • Seasonality decomposition is an extremely useful technique when working with time-series data.

Causal inference from observational data is vital to developing inference, since many difficult problems do not easily lend themselves to A/B testing. With a little thought, many designs in practice can be used to find causal factors.

Chapter 10 and 11 have demonstrated some of the most useful natural experimental and quasi-experimental designs for causal inference in observational data. The next chapter will cover statistical matching, another quasi-experimental design that’s more applicable to all cases, and Hill’s causality conditions, which we can use when all quasi-experimental designs fail.

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