Estimate Treatment Effects Using PSM, DiD, and DML Methods
Overview
This question evaluates a data scientist's competency in causal inference methods—covering propensity score matching, difference-in-differences, synthetic control, and double machine learning—and the ability to discuss identification assumptions, overlap/balance diagnostics, robustness checks, and handling of high-dimensional confounders.
Amazon
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Statistics & Math
13
0
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
- 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.
- 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.
- When is a Synthetic Control Method preferable to DiD? Provide the intuition and the key conditions that make it stronger than standard DiD.
- 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.
Solution
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