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I knew the high-level distinction but stumbled when they pushed on specific bias sources.
Start by clearly defining both study types, emphasizing their distinct goals: Brand Lift measures upper-funnel metrics like ad recall and brand awareness, while Conversion Lift measures lower-funnel actions like purchases or sign-ups. Then, systematically discuss the main sources of bias and variance for each, highlighting how they differ due to methodology (survey-based vs. behavioral) and experimental design. Conclude by relating these concepts to Pinterest's context, such as measuring ad effectiveness for visual discovery.
Pro tip: Show depth by mentioning that Brand Lift studies often use randomized control trials with survey-based outcomes, which introduces non-response bias, while Conversion Lift studies rely on user-level randomization and may suffer from dilution bias due to cross-device behavior. Also, note that Pinterest's unique position in visual search can leverage both, but requires careful handling of user privacy and measurement gaps.
Explain that Brand Lift measures the causal impact of ads on upper-funnel brand metrics such as ad recall, brand awareness, and favorability, typically via surveys with randomized control and treatment groups.
Explain that Conversion Lift measures the causal impact of ads on lower-funnel actions like purchases, sign-ups, or app installs, using randomized experiments and behavioral data, often with a ghost ads or intent-to-treat design.
Discuss biases such as non-response bias (survey respondents differ from non-respondents), selection bias if randomization fails, and measurement bias due to survey wording or timing.
Discuss biases like dilution (ads seen by control group via other channels), attribution errors (cross-device, view-through), and variance from low conversion rates, requiring large sample sizes and long test durations.
Summarize key differences: Brand Lift is survey-based and prone to self-report biases, while Conversion Lift is behavioral and prone to attribution and dilution biases; both require careful experimental design to minimize variance.
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This tripped me up more than it should have.
Start by defining what each study measures and why they might diverge, then walk through common scenarios where conflicts arise (e.g., upper-funnel vs. lower-funnel effects, measurement methodologies, attribution windows). Finally, outline a systematic reconciliation process that prioritizes understanding the root cause before deciding which metric to trust or how to combine insights.
Pro tip: Emphasize that conflicting results are often a signal to dig deeper into the data rather than a problem to be resolved by picking a winner. Mention that at Pinterest, where visual discovery often drives both brand and performance outcomes, reconciling these studies requires a nuanced understanding of user behavior across the funnel.
Explain that Brand Lift measures changes in awareness, ad recall, or favorability, while Conversion Lift measures incremental conversions. Highlight that they serve different purposes and may use different methodologies (e.g., survey-based vs. user-level conversion tracking).
List common reasons for divergence: different attribution windows, upper-funnel vs. lower-funnel focus, measurement bias (e.g., survey non-response), or external factors like seasonality. Also consider that brand lift may not immediately translate to conversions.
Dive into the details: check for statistical significance, sample sizes, and potential confounders. Compare the control groups and ensure both studies were properly randomized. Look for segment-level differences that might explain the conflict.
If both studies are valid, consider that they capture different aspects of the funnel. Use additional data (e.g., click-through rates, view-through conversions) to build a holistic view. If one study is flawed, address the limitations and possibly rerun with adjustments.
Present a clear narrative to stakeholders: acknowledge the conflict, explain the likely reasons, and recommend how to move forward (e.g., trust the more robust study, run a follow-up, or adjust KPIs). Emphasize the importance of aligning on the primary objective.
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DiD I was comfortable with, parallel trends assumption and all.
Start by defining each method and its core identification assumptions, then contrast them in the context of observational ads data. Discuss robustness checks that test the validity of these assumptions, and conclude with when to prefer one method over the other.
Pro tip: Emphasize that DiD relies on parallel trends, which is untestable but can be probed with pre-trend tests, while PSM assumes selection on observables, which is also untestable but can be assessed with balance checks. Mention that combining both (e.g., DiD with propensity score weighting) can strengthen causal inference when assumptions are partially met.
Briefly explain DiD (compares changes in outcomes over time between treated and control groups) and PSM (matches treated and control units based on propensity scores to create comparable groups).
For DiD: parallel trends assumption (absent treatment, treated and control groups would have followed parallel outcome trends). For PSM: conditional independence (selection on observables) and common support (overlap in propensity scores).
For DiD: test for pre-treatment trends, placebo tests, sensitivity analysis to violations. For PSM: check covariate balance after matching, sensitivity to unobserved confounders, and common support diagnostics.
Highlight that DiD is useful when you have pre/post data and a natural control group, while PSM is useful when you have rich covariates but no clear pre-period. Discuss potential biases in ads data (e.g., targeting, seasonality).
Suggest that the choice depends on data structure and assumptions; sometimes combining methods (e.g., DiD with PSM) can be more robust. Mention that in practice, you'd run both and compare results.
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The arithmetic is not hard but doing it live without a calculator while also explaining each step is a different experience.
First, compute the conversion rates for treatment and control, then calculate the absolute and relative lift. Next, compute the pooled standard error under the null hypothesis of no difference, and use it to calculate the z-statistic. Finally, compare the z-statistic to the critical value for a two-sided test at the 5% significance level (1.96) to determine significance.
Pro tip: Always clarify whether the question asks for absolute or relative lift, and mention that in practice you'd also check for novelty effects and ensure the experiment was run for a sufficient duration to detect meaningful effects.
Compute the conversion rate for treatment (42/1200 = 0.035) and control (33/1180 ≈ 0.02797). Then compute the absolute lift (difference) and relative lift (percentage change).
Under the null hypothesis of no difference, use the pooled conversion rate: (42+33)/(1200+1180) = 75/2380 ≈ 0.03151. Then compute the standard error as sqrt(p_pool*(1-p_pool)*(1/n_t + 1/n_c)).
Divide the difference in conversion rates by the pooled standard error to get the z-statistic.
Compare the absolute value of the z-statistic to the critical value for a two-sided test at α=0.05 (1.96). If |z| > 1.96, the result is statistically significant.
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This was the hardest part of the whole thing.
Start by clearly defining the four sample means and variances, then derive the difference-in-differences (DiD) estimator as the difference of the changes in means between exposed and control groups. Compute the standard error by combining the four independent variance terms scaled by their respective sample sizes, then calculate the t-statistic as the DiD estimate divided by its standard error. Show all formulas step-by-step to demonstrate rigor.
Pro tip: Emphasize that the DiD estimator assumes parallel trends and that the variance calculation treats the four group-time means as independent, which is valid for independent samples. Mention that in practice, you might use regression with interaction terms to obtain the same estimate and robust standard errors.
Label the four groups: exposed pre, exposed post, control pre, control post. Let their sample means be X̄_E,pre, X̄_E,post, X̄_C,pre, X̄_C,post and variances s²_E,pre, s²_E,post, s²_C,pre, s²_C,post with sample sizes n_E,pre, n_E,post, n_C,pre, n_C,post. Assume independent samples and that variances are known or estimated from large samples.
The DiD estimator is the difference between the change in the exposed group and the change in the control group: Δ = (X̄_E,post - X̄_E,pre) - (X̄_C,post - X̄_C,pre). This can also be written as X̄_E,post - X̄_E,pre - X̄_C,post + X̄_C,pre.
Since the four sample means are independent, the variance of Δ is the sum of the variances of each mean, each scaled by its sample size: Var(Δ) = s²_E,post/n_E,post + s²_E,pre/n_E,pre + s²_C,post/n_C,post + s²_C,pre/n_C,pre. The standard error is the square root of this sum.
The t-statistic is t = Δ / SE(Δ). Under the null hypothesis of no effect, t follows approximately a standard normal distribution for large samples. Report the t-statistic and optionally the p-value.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.