Instruction: Outline a study design using the DiD methodology to measure the causal impact of implementing a new coding standard policy across different teams within a tech company. Discuss potential data requirements, control and treatment group selection, and how you would address possible sources of bias.
Context: This question requires the candidate to demonstrate their understanding of the Difference-in-Differences (DiD) approach as a quasi-experimental design for causal inference. The candidate needs to show their ability to design a study that accounts for time and group differences, select appropriate control and treatment groups, and discuss how to manage confounders and biases in the study setup.
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To begin with, let’s clarify the question: We aim to estimate the causal effect of implementing a new coding policy on developer productivity across different teams within a tech company using the DiD approach.
First, we need to carefully define our treatment and control groups. For the treatment group, we would select teams that will adopt the new coding policy. Ideally, these teams should be chosen based on their similarity to other teams in terms of size, composition, type of projects, and baseline productivity levels to ensure comparability. The control group would consist of teams that continue with the existing coding standards over the same time period....
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