This one stung a little because I started with pre-post analysis and the interviewer just waited.
Acknowledge the limitation of no control group and propose quasi-experimental methods like difference-in-differences, synthetic control, or interrupted time series. Outline the data needed (pre/post metrics, covariates, potential control groups) and the assumptions (parallel trends, no concurrent shocks). Communicate uncertainty via confidence intervals and sensitivity analyses.
Pro tip: Emphasize that the best approach depends on the specific context—e.g., whether a natural control group exists (like a holdout in another region) or if you can use pre-launch trends. Always discuss the trade-offs between different methods and the importance of validating assumptions.
Understand what decision the causal estimate will inform and what data is available. Identify if any natural control groups exist (e.g., users who couldn't access the feature due to technical issues, or similar markets).
Select an appropriate method based on data availability: difference-in-differences (if a control group exists), synthetic control (if multiple pre-periods and a donor pool), interrupted time series (if only one unit), or propensity score matching (if covariates available).
List data needed: pre- and post-launch metrics for treated and control units, covariates for matching, and time series data. State key assumptions: parallel trends, no spillover, no concurrent events, and correct model specification.
Apply the method, check assumptions (e.g., pre-trends for DiD), and run sensitivity analyses (e.g., placebo tests, varying control groups). Quantify uncertainty with confidence intervals or Bayesian credible intervals.
Present the estimate with uncertainty ranges, explain assumptions and their plausibility, and discuss how violations would affect conclusions. Recommend follow-up experiments if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.