Evaluate Propensity Score Matching Alternatives and Diagnostics

Quick Overview

This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Evaluate Propensity Score Matching Alternatives and Diagnostics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Evaluate Propensity Score Matching Alternatives and Diagnostics

Company: Netflix

Role: Data Scientist

Category: Statistics & Math

Difficulty: hard

Interview Round: Other

##### Scenario Reviewing a completed observational study that used Propensity Score Matching (PSM) to estimate the impact of a UI change on watch time. ##### Question Why is standardized mean difference (SMD) ≤ 0.1 often used as a balance threshold in PSM? If logistic regression is not appropriate for the propensity model, what alternatives would you consider and why? How would you diagnose residual confounding after matching? Describe one method to estimate treatment effect variance under PSM. ##### Hints Discuss overlap, balance diagnostics, causal estimands, and robust variance.

Quick Answer: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Evaluate Propensity Score Matching Alternatives and Diagnostics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Evaluate Propensity Score Matching Alternatives and Diagnostics

Context

You are reviewing an observational study that used Propensity Score Matching (PSM) to estimate the causal impact of a UI change on user watch time. Randomized experimentation was not feasible, so historical logs and user covariates were leveraged to construct a matched sample and estimate an ATT (average treatment effect on the treated).

Task

Answer the following about best practices in PSM for product analytics:

  1. Why is standardized mean difference (SMD) ≤ 0.1 often used as a post-matching balance threshold?
  2. If logistic regression is not appropriate for the propensity model, what alternatives would you consider and why?
  3. How would you diagnose residual confounding after matching?
  4. Describe one method to estimate the variance of the treatment effect under PSM and when it is appropriate.

Hint: Discuss overlap/positivity, balance diagnostics (including higher moments and transformations), causal estimands (ATE vs ATT), and robust variance estimation.

Clarifying Questions to Ask Guidance

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers Guidance

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions Guidance

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
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