This is where I spent most of my mental energy.
Acknowledge the selection bias from opt-in power users, then recommend Difference-in-Differences (DiD) as the most practical choice, while briefly noting why the others are less suitable. Walk through the key identifying assumptions—parallel trends, no anticipation, and no spillovers—and discuss how to test or mitigate violations.
Pro tip: Emphasize that the parallel trends assumption is untestable but can be supported by pre-trend analysis and placebo tests; also mention that if power users are fundamentally different, consider combining DiD with propensity score matching (PSM) to create a more comparable control group.
Explain that opt-in power users are not representative, so randomization is impossible and naive comparisons would be biased. This sets the stage for why a quasi-experimental method is needed.
Select DiD because it controls for time-invariant differences between power users and others, and can leverage a natural experiment (e.g., a policy change or feature rollout) that affects one group but not the other. Briefly contrast with IV (needs a valid instrument), Synthetic Control (needs a donor pool and pre-period fit), and PSM (only controls for observed confounders).
List the core DiD assumptions: parallel trends (absent treatment, the outcome trends would be the same for both groups), no anticipation effects, and no spillovers (treatment of one group does not affect the other). Also mention stable composition and no other concurrent shocks.
Describe how to test assumptions: plot pre-treatment trends, run placebo tests (e.g., fake treatment dates), check for anticipation, and consider robustness checks like changing the control group or using synthetic control as a sensitivity analysis.
Admit that DiD may not fully address unobserved time-varying confounders. If parallel trends is questionable, suggest combining with PSM or using synthetic control if a good donor pool exists. Mention that IV could be used if a valid instrument is available.
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Talked through running the analysis on a period before the feature launched to see if the treatment effect shows up where it shouldn't.
Start by clarifying the chosen method (e.g., difference-in-differences, synthetic control, or switchback) and the causal assumptions it relies on. Then explain how you would design placebo tests and pre-trend checks to validate those assumptions, and finally outline a set of robustness checks to probe sensitivity to violations and alternative specifications.
Pro tip: Emphasize that pre-trend checks are necessary but not sufficient—placebo tests on unaffected outcomes or pre-periods can catch violations that pre-trends miss. Also, mention that you would pre-register your robustness checks to avoid p-hacking.
Briefly state the chosen method (e.g., DiD, synthetic control) and the key identifying assumptions, such as parallel trends or no anticipation. This sets the foundation for the tests you propose.
Explain how you would test for pre-existing trends, e.g., event-study plots, placebo tests on pre-periods, or formal tests like the parallel trends test. Mention that you would check for differential trends in the pre-treatment period.
Describe placebo tests such as using a fake treatment date, a fake treatment group, or an unaffected outcome. Explain how these tests can detect violations of assumptions and provide evidence for the validity of the main results.
List robustness checks such as alternative specifications, different control groups, sensitivity analysis (e.g., Rosenbaum bounds), and testing for heterogeneous effects. Mention that you would also check for spillover effects and anticipation.
Discuss how you would interpret the results of these tests and communicate them to stakeholders, including any limitations and potential biases. Emphasize transparency and reproducibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Negative controls I had a decent answer for: pick an outcome the feature logically shouldn't affect and check if your method still finds an effect.
Start by framing sensitivity analysis as a way to test the robustness of causal conclusions to unmeasured confounding and modeling assumptions. Then, describe specific techniques like negative controls and Rosenbaum bounds, explaining when and how you would apply them. Finally, tie it back to the business context at Meta, emphasizing how these methods build trust in experimental results.
Pro tip: Emphasize that sensitivity analyses are not just statistical exercises but tools to communicate uncertainty to stakeholders and guide decision-making. Show that you can balance rigor with practicality by prioritizing analyses that address the most plausible threats to validity.
Restate the goal of the analysis and the key identifying assumptions (e.g., no unmeasured confounding, correct model specification). Explain that sensitivity analyses test how violations of these assumptions affect conclusions.
Discuss potential biases (e.g., unmeasured confounding, selection bias, measurement error) and select appropriate sensitivity analyses such as negative controls, Rosenbaum bounds, or E-values. Justify your choices based on the context.
Describe how negative control outcomes or exposures can detect unmeasured confounding. Give an example: if a negative control outcome shows an effect, it suggests bias. Explain how to interpret results and adjust if needed.
Detail how Rosenbaum bounds quantify the strength of unmeasured confounding needed to overturn a significant result. Explain the sensitivity parameter (Gamma) and how to interpret it (e.g., Gamma=1.5 means confounder would need to increase odds of treatment by 50% to explain away effect).
Summarize how you would integrate results from multiple sensitivity analyses to assess robustness. Discuss how to present findings to stakeholders, including limitations and confidence in causal claims.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the stakeholder's need for a clear, non-technical explanation, then use a relatable analogy to illustrate how quasi-experimental methods can systematically over- or under-estimate effects compared to randomization. Emphasize that randomization is the gold standard because it balances all factors, while quasi-experiments may leave residual confounding that biases results in a particular direction.
Pro tip: Use a concrete example from the stakeholder's domain (e.g., ad campaigns) to make the bias tangible, and always quantify the risk when possible (e.g., 'we might overestimate lift by 10-20%') to build trust and show business impact.
Briefly explain why we use quasi-experimental methods (e.g., when randomization isn't feasible) and that they aim to estimate causal effects but can be biased.
Use a simple analogy, like comparing two groups of plants where one gets more sunlight, to show how unaccounted differences can skew conclusions.
Clarify that bias can go either way: we might think an effect is larger or smaller than it truly is, and this direction depends on the unmeasured factors.
Highlight that randomization acts like a fair coin flip, balancing all factors (known and unknown) on average, so any difference is likely due to the treatment.
Explain how this bias could affect decisions, and mention ways to assess or reduce it (e.g., sensitivity analysis, triangulation with other methods).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.