Balancing Assumptions and Data When Using IV

In previous videos we said that the relevance assumption is refutable, and in this video we saw that it passed our tests in the Oregon Healthcare experiment. We also said that the other two assumptions, exogeneity and exclusion, are nonrefutable because we would need a world of complete and perfect information to refute them.

But two data analysts, Alex and Monique, are working together on an IV analysis, and they argue about whether more data can help them build a convincing argument for their instrument. Alex says, "there's no point to changing our model to include more datasets, because these last 2 assumptions are nonrefutable, so we can only use qualitative explanations to defend them." But Monique says that "we can replace our qualitative arguments with data-driven arguments if we add new datasets with the relevant data to our model."

Who is giving better advice for defending the 'nonrefutable' assumptions of IV?

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