Acknowledge the lack of a holdout and propose a quasi-experimental design, such as difference-in-differences or synthetic control, to estimate the causal effect. Emphasize the importance of validating assumptions, quantifying uncertainty, and communicating limitations transparently to stakeholders.
Pro tip: Frame the analysis as a retrospective causal inference study, not an A/B test, and proactively suggest a follow-up randomized experiment to confirm findings. This shows you understand both the limitations of observational data and the value of experimentation.
Define the target metric (e.g., disconnection rate) and identify available pre- and post-launch data, including potential control groups (e.g., unaffected regions, similar products, or users who haven't upgraded).
Select an appropriate causal inference technique like difference-in-differences, synthetic control, or interrupted time series, based on data structure and plausibility of assumptions.
Articulate key assumptions (e.g., parallel trends, no spillovers) and perform diagnostics (e.g., pre-trend tests, placebo tests) to assess their validity.
Control for observed confounders via matching or regression, and conduct sensitivity analyses (e.g., different control groups, model specifications) to check robustness.
Report effect estimates with confidence intervals, discuss limitations, and use clear visualizations to convey uncertainty to stakeholders, recommending next steps like a randomized experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.