Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon data scientist interview focused heavily on causal inference methodology. One big question that covered a lot of ground, felt like a mini take-home crammed into a verbal answer.

Questions Asked (1)

Walk through how you would use Propensity Score Matching and Difference-in-Differences to estimate the causal effect of a marketing campaign. When would you prefer a synthetic control approach over DiD? Also explain the Double Machine Learning framework and why it handles high-dimensional covariates better.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This was a lot to unpack in one question.

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AI HintsAI Generated

Suggested Approach

Structure your answer by first explaining how you would use PSM to create a comparable control group, then apply DiD to estimate the treatment effect by comparing pre-post changes. Next, discuss when synthetic control is preferable (e.g., single treated unit, few controls, poor parallel trends) and finally introduce DML as a robust method for high-dimensional confounders, highlighting its use of orthogonalization and cross-fitting.

Pro tip: Emphasize that the validity of causal claims hinges on assumptions like unconfoundedness (PSM) and parallel trends (DiD), and always discuss sensitivity analyses or placebo tests to check robustness. For Amazon, tie your answer to practical scenarios like measuring the impact of a Prime Day campaign or a new feature rollout.

1. Propensity Score Matching (PSM)

Describe how to estimate propensity scores using logistic regression or ML models, then match treated and control units to balance covariates. Mention checking balance and common support.

2. Difference-in-Differences (DiD)

Explain the DiD setup: compare changes in outcomes over time between treated and control groups, assuming parallel trends. Highlight the need for pre-period data and testing the parallel trends assumption.

3. When to prefer Synthetic Control

Discuss scenarios like a single treated unit (e.g., one region) with multiple control units, where DiD's parallel trends assumption is unlikely to hold. Synthetic control constructs a weighted combination of controls to mimic the treated unit's pre-treatment trajectory.

4. Double Machine Learning (DML) Framework

Explain DML: it uses ML models to estimate nuisance functions (e.g., outcome and treatment models) and then applies orthogonalization (e.g., partialling out) to obtain a debiased treatment effect estimate. Cross-fitting avoids overfitting.

5. Why DML handles high-dimensional covariates

Highlight that DML allows flexible ML models to control for many covariates without strict functional form assumptions, and the orthogonalization reduces regularization bias, leading to valid inference even when the number of covariates is large relative to sample size.

Key Points to Mention

Assumptions: unconfoundedness for PSM, parallel trends for DiD, and no interference (SUTVA).
Propensity score estimation and matching methods (e.g., nearest neighbor, caliper).
DiD regression specification with time and group fixed effects, and interaction term.
Synthetic control method: construction of synthetic control via weighted average of control units, placebo tests for inference.
Double Machine Learning: Neyman orthogonality, cross-fitting, and use of ML models like random forests or neural nets for nuisance estimation.
Comparison of methods: PSM and DiD are simpler but rely on strong assumptions; synthetic control is useful for case studies; DML is robust for high-dimensional settings but requires more data and computational resources.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.