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Start by acknowledging the missing control group and the need for a quasi-experimental design. Then propose at least two observational methods, such as difference-in-differences (DiD) and synthetic control, and compare their assumptions, data requirements, and robustness. Emphasize the importance of validating assumptions and quantifying uncertainty.
Pro tip: When using synthetic control, consider using pre-period data to construct a donor pool from similar markets or user segments, and always conduct placebo tests to assess the method's reliability.
Briefly restate the scenario: no control group, 5 days of treatment for all eligible users, and 4 weeks of pre-period data. Highlight that we need to estimate the causal effect using observational methods.
Explain how DiD can be used if a suitable control group (e.g., non-eligible users or similar markets) exists. Discuss the parallel trends assumption and how to test it using pre-period data.
Describe how SCM constructs a weighted combination of untreated units (e.g., other markets or user segments) to serve as a control. Mention the need for a donor pool and pre-period fit.
Compare DiD and SCM in terms of assumptions (parallel trends vs. convex hull), data requirements (need for control group vs. donor pool), and robustness. Discuss when each is more appropriate.
Emphasize the importance of placebo tests, robustness checks, and quantifying uncertainty (e.g., confidence intervals via bootstrap). Mention potential confounders like seasonality or external events.
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Parallel trends for diff-in-diff, ignorability for PSW, no interference for basically everything.
Start by clarifying which methods you're comparing (e.g., A/B testing, observational studies, quasi-experiments) and explicitly state the assumptions each relies on. Then, for each assumption, propose a falsification or placebo test that would detect violations, emphasizing how you'd implement them in a Pinterest-like environment.
Pro tip: Frame your answer around the trade-off between statistical power and validity: placebo tests often reduce power, so you need to balance rigor with practical constraints. Mention that you'd pre-register these tests to avoid p-hacking.
Identify the specific methods (e.g., A/B test, difference-in-differences, propensity score matching) and list the key assumptions each makes (e.g., SUTVA, parallel trends, ignorability).
For each assumption, describe what a violation would look like in practice and how it could bias results (e.g., interference, confounding, spillover effects).
Propose specific tests: e.g., A/A tests for A/B testing, pre-trend tests for DiD, negative control outcomes for observational studies. Explain how to implement and interpret them.
Acknowledge that placebo tests are not definitive and may have low power; discuss how to balance rigor with practical constraints like sample size and timeline.
Give concrete examples relevant to Pinterest, such as testing for network effects in social features or using holdout groups for long-term effects.
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ATT vs ATE distinction I handled okay since the treatment was forced on all eligible users, ATT is really what you're estimating anyway.
Start by defining ATT and ATE in the context of the experiment, then explain how to estimate them using appropriate methods like CUPED or regression adjustment. Address the short 5-day window by discussing techniques to mitigate seasonality, novelty effects, and calendar confounds, such as using pre-period data, day-of-week adjustments, and holdout groups.
Pro tip: Emphasize the importance of pre-experiment covariate adjustment (e.g., CUPED) to reduce variance and control for confounds, and mention that extending the analysis window or using a switchback design can help isolate novelty effects.
Clarify that ATT (Average Treatment Effect on the Treated) measures the effect for those actually exposed to the treatment, while ATE (Average Treatment Effect) measures the effect for the entire population. In A/B tests, random assignment typically estimates ATE, but if there is non-compliance or differential exposure, ATT may be more relevant.
Use regression adjustment or propensity score weighting to estimate ATT and ATE. For ATE, a simple difference in means between treatment and control is unbiased under randomization. For ATT, consider instrumental variables or weighting by exposure probability.
Include day-of-week and time-of-day fixed effects in your model, or use a matched control group from a similar time period. If possible, compare with historical data to adjust for seasonal trends.
Analyze the treatment effect over time within the 5-day window to detect novelty spikes. Use a holdout group that continues beyond the window to see if effects persist. Consider using a pre-period to establish baseline behavior.
Apply variance reduction techniques like CUPED using pre-experiment covariates to control for confounds and increase sensitivity, which is crucial for short experiments.
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Cluster-robust standard errors if the data has user-level clustering over time, bootstrap otherwise especially under PSW since the weights themselves are estimated.
Start by acknowledging that observational studies require careful uncertainty quantification due to confounding and selection bias. Then outline how you would use weighting or matching to estimate causal effects, and describe methods to quantify uncertainty such as bootstrap, robust standard errors, or sensitivity analysis. Emphasize the importance of validating assumptions and communicating uncertainty to stakeholders.
Pro tip: Always pair your point estimate with a sensitivity analysis (e.g., E-value or Rosenbaum bounds) to show how robust your findings are to unmeasured confounding—this demonstrates rigor and maturity beyond just statistical significance.
Define the causal effect of interest (e.g., ATE, ATT) and state the key identifying assumptions such as conditional exchangeability, positivity, and consistency. This sets the foundation for uncertainty quantification.
Select an appropriate method (e.g., IPW, propensity score matching, doubly robust estimation) based on the estimand and data structure. Explain how the method balances covariates and reduces confounding.
Use bootstrap (especially for matching or complex weights) or robust sandwich standard errors (for IPW) to account for the estimation of weights and matching. For matching, consider Abadie-Imbens standard errors.
Perform sensitivity analysis (e.g., E-value, Rosenbaum bounds) to quantify how strong an unmeasured confounder would need to be to alter conclusions. This addresses a major source of uncertainty in observational studies.
Present confidence intervals, sensitivity results, and caveats clearly to stakeholders. Discuss how uncertainty might impact business decisions and suggest further validation (e.g., negative controls, A/B tests).
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This felt like the 'culture fit meets process design' part of the question.
Acknowledge the issue without defensiveness, then systematically walk through the root causes of the flawed experiment setup and propose concrete prevention mechanisms. Emphasize cross-functional collaboration and a culture of learning, showing how you would turn the failure into a process improvement.
Pro tip: Frame prevention mechanisms as scalable, automated guardrails (e.g., pre-launch checklists, automated validation) rather than one-off fixes, and highlight how you'd communicate the changes to build trust across teams.
Start by recognizing the mistake and its impact, demonstrating accountability without blaming others. This sets a constructive tone.
Identify the specific flaws in the experiment setup (e.g., sampling bias, metric misalignment, insufficient power) and explain how they led to the problem.
Propose concrete, actionable steps to prevent recurrence, such as automated validation checks, peer reviews, and standardized experiment design templates.
Explain how you would collaborate with engineering, product, and other stakeholders to implement and monitor these mechanisms.
Describe how you would track the effectiveness of the new processes and iterate, fostering a culture of experimentation and learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.