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10  Complex Diff-in-Diff Designs – Causal Inference<br/>*The Remix*

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In this chapter, we are going to build on the more complex situations where the parallel trends assumption does not hold, but that can be “fixed” while still retaining a design approach to the problem. We will also cover sensitivity tests we can review if we think that parallel trends is maybe violated, but we want to see how bad it has to be before we probably cannot say much. And finally, we will discuss a popular situation in which the treatment hits groups of units at different points in time called “differential timing.” I will conclude the chapter with a data exercise and a series of steps to consider when undertaking your own analysis. As always, we will accompany the discussion with applications and code in the hopes that it makes the ideas concrete, and provides some programming blueprints for your own work. Diff-in-diff works with longitudinal data of which there are two types: panel data and repeated cross-sections. Panel datasets follow the same individual units over time,

In this chapter, we are going to build on the more complex situations where the parallel trends assumption does not hold, but that can be “fixed” while still retaining a design approach to the problem. We will also cover sensitivity tests we can review if we think that parallel trends is maybe violated, but we want to see how bad it has to be before we probably cannot say much. And finally, we will discuss a popular situation in which the treatment hits groups of units at different points in time called “differential timing.” I will conclude the chapter with a data exercise and a series of…

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