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Causal inference when you can't experiment: difference-in-differences and synthetic controls

Linear Digressions

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Introduction

In causal inference, we're trying to study treatments and whether they cause differences in outcomes across like people or regions. The gold standard way that usually we'd like to study causal inference is by running a randomized control trial so it's really hard to know in general when you see different outcomes. Like it can be r tricky to understand, like causal effects in that situation.

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