Meta·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Meta DS interview with a brutally detailed causal inference question about measuring brand advertising effectiveness on social media. One question, but it had like nine sub-parts and I'm pretty sure I was still answering it when they cut me off.

Questions Asked (1)

You're given the hypothesis that social media advertising is less effective for brand building than other channels. You have historical multi-channel data and can run experiments. Walk through a full causal measurement plan: how you'd define brand outcomes, what experiment designs you'd consider, how you'd power the test, what model you'd pre-register, how you'd handle budget confounding, what guardrails you'd set, how you'd approach subgroup analysis, and what your final decision rule would look like.

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Strategy

Author's notes

This was one of those questions where you finish your first answer and realize you've only covered maybe a third of what they actually wanted.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Structure your answer around a causal inference framework: start by defining brand outcomes with both survey-based and behavioral metrics, then propose a randomized experiment (e.g., geo-based or user-level) with pre-registered analysis, power calculations, and guardrails. Address budget confounding by design (randomization) and analysis (covariate adjustment), and end with a clear decision rule that balances statistical significance with practical significance.

Pro tip: Emphasize pre-registration and pre-specified decision rules to avoid p-hacking and demonstrate scientific rigor; also, mention that brand outcomes often require long-term measurement, so consider proxy metrics and holdout designs.

1. Define Brand Outcomes

Select a combination of survey-based metrics (e.g., brand awareness, consideration, favorability) and behavioral proxies (e.g., direct traffic, branded search volume) that capture brand building. Ensure they are measurable, sensitive to change, and aligned with business goals.

2. Choose Experiment Design

Consider randomized controlled trials: user-level randomization if possible, otherwise geo-based or cluster randomization. For social media ads, a ghost ads or PSA control can isolate ad effect. Ensure randomization unit matches analysis unit and accounts for spillover.

3. Power Analysis and Pre-registration

Conduct power analysis based on expected effect size, variance, and desired power (80%) and significance (5%). Pre-register the primary model (e.g., linear regression with covariates), subgroup analyses, and decision criteria to prevent post-hoc bias.

4. Handle Confounding and Guardrails

Randomization addresses budget confounding, but if not feasible, use propensity score matching or instrumental variables. Set guardrails: monitor for negative effects on other channels, ad fatigue, and ensure budget reallocation doesn't cannibalize.

5. Subgroup Analysis and Decision Rule

Pre-specify subgroups (e.g., demographics, prior exposure) and adjust for multiple comparisons. Final decision: if social media shows a statistically significant and practically meaningful lift in brand outcomes vs. other channels, adopt; otherwise, reallocate budget.

Key Points to Mention

Use of both survey-based brand lift metrics and behavioral proxies to measure brand outcomes.
Randomized experiment designs: user-level, geo-based, or cluster randomization, with ghost ads as control.
Power analysis to determine sample size, considering intra-cluster correlation for geo tests.
Pre-registration of analysis plan, including primary model, subgroups, and decision thresholds.
Address budget confounding via randomization or causal inference methods like propensity score matching.
Guardrails: monitor for cannibalization, ad fatigue, and negative spillovers; use holdout groups for long-term effects.

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