causal inference

Shipping a model is an experiment, and this question separates people who run A/B tests from people who p-hack them. The signal is pre-registering the metric, sizing the test, and resisting the urge to peek.

Senior DS loops test causal reasoning past the randomized A/B test. The signal is naming the quasi-experimental method that fits and stating the single assumption it lives or dies on. Here is the answer for when randomization is off the table.

IV is the causal tool people name but can't defend. The signal is articulating the two conditions an instrument must satisfy, why one is testable and one is not, and what 2SLS actually estimates. Here is the answer.

Propensity scores promise to mimic an experiment from observational data. The signal is knowing what they can and cannot fix, the overlap trap, and why IPW blows up. Here is the answer that separates careful candidates.

A response model predicts who will convert; an uplift model predicts who converts because of the treatment. The signal is the four-quadrant intuition and how you evaluate uplift when you never observe an individual's lift. Here is the answer.