TikTok·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

TikTok data scientist interview with a causal inference question that felt more like a mini research design exercise than a typical DS screen. The question had real depth to it and pushed me to think carefully about selection bias in a product context.

Questions Asked (1)

Management wants to understand how negative customer reviews affect whether a user buys the same coupon again. How would you design an analysis to measure this effect, and what methods would you use to separate the review's impact from other confounding factors?

A/B Testing & ExperimentationProduct Analytics & MetricsRoot Cause Analysis

Author's notes

This one took me a second to frame properly.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a causal inference question: does exposure to negative reviews causally reduce repeat purchase of the same coupon? Propose a mix of observational methods (e.g., propensity score matching, fixed effects) and experimental designs (e.g., A/B test with review sentiment manipulation) to isolate the effect, while controlling for confounders like user history, coupon quality, and selection bias.

Pro tip: Emphasize that correlation isn't causation: users who leave negative reviews may differ systematically from those who don't, so you need to account for selection bias. Also, consider that the effect may be heterogeneous—negative reviews might matter more for new users than loyal ones.

1. Define the causal question and metrics

Clarify the treatment (exposure to negative reviews) and outcome (repeat purchase of the same coupon). Define negative reviews operationally (e.g., rating ≤ 2 stars) and specify the time window for repeat purchase.

2. Identify confounders and data sources

List potential confounders: user demographics, past purchase behavior, coupon characteristics, review volume, and time trends. Determine what data is available (e.g., user logs, review text, transaction records).

3. Choose identification strategy

If randomization is possible, design an A/B test where users are randomly exposed to negative reviews (e.g., by manipulating review display). If not, use quasi-experimental methods like propensity score matching, difference-in-differences, or instrumental variables.

4. Control for confounders and estimate effect

Apply statistical models (e.g., logistic regression with fixed effects, causal forests) to estimate the treatment effect while adjusting for confounders. Check for heterogeneous treatment effects across user segments.

5. Validate and interpret results

Conduct robustness checks (e.g., placebo tests, sensitivity analysis) and assess practical significance. Communicate findings with confidence intervals and discuss limitations.

Key Points to Mention

Causal inference methods: propensity score matching, difference-in-differences, instrumental variables, or randomized experiments.
Confounders: user past behavior, coupon attractiveness, review volume, selection bias (who writes reviews), and time trends.
Heterogeneous treatment effects: analyze how the impact varies by user segment (e.g., new vs. loyal users).
Metrics: define repeat purchase rate, time to repeat purchase, and review sentiment scores.
Experimental design: if feasible, run an A/B test where users are randomly assigned to see negative reviews vs. not.
Robustness checks: placebo tests, sensitivity analysis, and validation against holdout data.

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