Causal Inference Without Clean Experiments

Scenario

You are evaluating a product change on a feed or notification surface, but rollout was not randomized and adoption was correlated with user behavior. You still need to estimate whether the change caused a shift in outcomes rather than just reflecting selection or timing effects.

Question

How would you think about causal inference in a product setting when you can’t run a clean experiment?

Problem

Scenario

Question

How would you think about causal inference in a product setting when you can’t run a clean experiment?

What this tests

Choosing a causal identification strategy in observational product data
Using regression and time-based comparisons to reduce bias
Explaining assumptions behind difference-in-differences
Separating statistical evidence from causal credibility

Problem

Scenario

Question

How would you think about causal inference in a product setting when you can’t run a clean experiment?

What this tests

Choosing a causal identification strategy in observational product data
Using regression and time-based comparisons to reduce bias
Explaining assumptions behind difference-in-differences
Separating statistical evidence from causal credibility

Problem

Scenario

Question

How would you think about causal inference in a product setting when you can’t run a clean experiment?

What this tests

Choosing a causal identification strategy in observational product data
Using regression and time-based comparisons to reduce bias
Explaining assumptions behind difference-in-differences
Separating statistical evidence from causal credibility

Interview Guides

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Scenario

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What this tests

Problem

Scenario

Question

What this tests

Causal Inference Without Clean Experiments

Problem

Scenario

Question

What this tests

Problem

Scenario

Question

What this tests