self-selected treatments

Choosing the Right Causal Model When Users Opt In

Choosing the right causal model when users opt in isn't a theoretical exercise, it's the difference between trustworthy insights and misleading conclusions. This simulation study tackles the practical question of Difference-in-Differences versus Lagged-Outcome Regression for self-selected treatments in product analytics, offering a pre-period check to guide your choice. For anyone wrestling with case study design, our related piece on spotting the four case study types in data science interviews provides complementary context worth exploring.