Explain propensity score methods: matching, weighting (IPW), and the overlap assumption. When do they fail?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
