Estimate Treatment Effects Using PSM, DiD, and DML Methods

Quick Overview

Estimate Treatment Effects Using PSM, DiD, and DML Methods evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Estimate Treatment Effects Using PSM, DiD, and DML Methods

Company: Amazon

Role: Data Scientist

Category: Statistics & Math

Difficulty: hard

Interview Round: Onsite

##### Scenario Marketing analytics team wants to measure the causal impact of campaigns. ##### Question Explain how you would use Propensity Score Matching (PSM) and Difference-in-Differences (DiD) to estimate treatment effect. When is a synthetic control method preferable to DiD? Describe the Double Machine Learning (DML) framework for causal inference and why it helps with high-dimensional covariates. ##### Hints Cover identification assumptions, overlap, parallel trends, cross-fitting, and robustness checks.

Quick Answer: Estimate Treatment Effects Using PSM, DiD, and DML Methods evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer 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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Estimate Treatment Effects Using PSM, DiD, and DML Methods

Causal Impact of Marketing Campaigns: PSM, DiD, Synthetic Control, and DML

Scenario

You have observational data from a marketing campaign where some users/regions were exposed to a campaign (treatment) and others were not (control). You also have outcomes measured before and after the campaign for each unit.

Tasks

  1. Explain how you would use Propensity Score Matching (PSM) to estimate the treatment effect. Specify assumptions, how you would check overlap and balance, and what robustness checks you would run.
  2. Explain how you would use Difference-in-Differences (DiD) to estimate the treatment effect. State identification assumptions (e.g., parallel trends), how you would implement it in practice, and robustness checks.
  3. When is a Synthetic Control Method preferable to DiD? Provide the intuition and the key conditions that make it stronger than standard DiD.
  4. Describe the Double Machine Learning (DML) framework for causal inference, focusing on why it is useful with high-dimensional covariates. Include the role of cross-fitting and orthogonalization, and the required assumptions.

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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