How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Google.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Google during technical interviews.

How would you use propensity score matching here

Quick Overview

Evaluates proficiency with propensity score matching and broader causal inference concepts for observational treatment effect estimation, including identification assumptions, propensity score estimation and covariate selection, matching algorithms and common support, balance diagnostics, choice of estimand (ATE vs ATT) and uncertainty quantification, plus sensitivity checks and alternative methods. Common in Analytics & Experimentation interviews for Data Scientist roles because it probes understanding of confounding and identification in nonrandomized settings and requires intermediate-to-advanced applied statistics reasoning about methodological trade-offs.

You want to estimate the causal effect of a new recommender feature on 7-day retention.

The feature was not randomized: users “opt in” after seeing a prompt, and more engaged users are more likely to opt in. You have observational data with columns:

user_id
opt_in (0/1)
pre-period covariates (e.g., prior watch time, sessions, country, device)
outcome: retained_7d (0/1)

Explain how you would use propensity score matching (PSM) to estimate the treatment effect. Address:

Identification assumptions (and what can break them).
How to estimate propensity scores and choose covariates.
Matching method and common support.
Balance diagnostics.
How to estimate ATE vs ATT and compute uncertainty.
Sensitivity analyses and alternatives (e.g., weighting, doubly robust, IV) if PSM is weak.

Quick Overview

You want to estimate the causal effect of a new recommender feature on 7-day retention.

The feature was not randomized: users “opt in” after seeing a prompt, and more engaged users are more likely to opt in. You have observational data with columns:

user_id
opt_in (0/1)
pre-period covariates (e.g., prior watch time, sessions, country, device)
outcome: retained_7d (0/1)

Explain how you would use propensity score matching (PSM) to estimate the treatment effect. Address:

Identification assumptions (and what can break them).
How to estimate propensity scores and choose covariates.
Matching method and common support.
Balance diagnostics.
How to estimate ATE vs ATT and compute uncertainty.
Sensitivity analyses and alternatives (e.g., weighting, doubly robust, IV) if PSM is weak.

How would you use propensity score matching here

Quick Overview

Solution

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How would you use propensity score matching here

Quick Overview

Solution

Comments (0)