Estimate Treatment Effects Using PSM, DiD, and DML Methods

Q: Estimate Treatment Effects Using PSM, DiD, and DML Methods

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.

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Question

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.

Estimate Treatment Effects Using PSM, DiD, and DML Methods

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

Scenario

Tasks

Solution

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