causal inference

Shipping a model is itself an experiment, and this question tells apart people who run A/B tests from those who p-hack them. The signal is pre-registering the metric, sizing the test, and holding back from peeking.

Senior DS loops test causal reasoning beyond the randomized A/B test. What interviewers reward 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 cannot defend. The signal is spelling out the two conditions an instrument must meet, why one is testable and one is not, and what 2SLS actually estimates. Here is the answer.

Propensity scores promise to imitate 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 sets careful candidates apart.

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 an individual's lift is never observed. Here is the answer.