Explain Propensity Score Matching and Assess Covariate Balance

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 Explain Propensity Score Matching and Assess Covariate Balance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Explain Propensity Score Matching and Assess Covariate Balance

Company: Amazon

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Phone interview emphasising causal inference techniques. ##### Question What is Propensity Score Matching (PSM)? List its main assumptions, outline the implementation steps, and describe how you would assess covariate balance after matching. ##### Hints Discuss unconfoundedness, common support, caliper, and standardized mean differences.

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 Explain Propensity Score Matching and Assess Covariate Balance 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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Explain Propensity Score Matching and Assess Covariate Balance

Propensity Score Matching (PSM)

Context

You have observational data with a binary treatment (T ∈ {0,1}), an outcome (Y), and a set of pre-treatment covariates (X). You want to estimate the causal effect of treatment on the outcome while adjusting for confounding.

Question

  • What is Propensity Score Matching (PSM)?
  • List its main assumptions.
  • Outline the implementation steps.
  • Describe how you would assess covariate balance after matching.

Hints: Discuss unconfoundedness, common support, caliper, and standardized mean differences (SMD).

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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