You observe a positive correlation between ad page visits and the probability an ad is reported as bad. Investigate whether more page visits cause more reports, or whether exposure/selection confounds the relationship. Provide: (1) at least three concrete hypotheses (e.g., pure exposure effect, targeting bias to sensitive cohorts, low-quality ads drive both attention and reporting, instrumentation effects), and the falsifiable prediction each makes; (2) a diagnostic plan including exact metrics and cuts: per-visit report rate vs. visit volume buckets; within-ad fixed-effects trends; cohorting by traffic source; funnel-based conditional probabilities; (3) a statistical model to estimate effect size controlling for exposure: e.g., ad-day logistic or Poisson regression with an offset log(visits), ad fixed effects, hour-of-day and geo controls; report the coefficient interpretation and how you would check overdispersion and multicollinearity; (4) at least one quasi-experimental approach (e.g., holdout geos, randomized delivery caps, or an IV using exogenous traffic shocks like site outages) and how you’d run a parallel A/A to validate; (5) criteria for action: define thresholds for an operational alert (e.g., ≥20% lift in per-visit report rate with p<0.01 after multiple-testing control) and what product/policy levers you’d recommend under each outcome.

This question evaluates a candidate's competency in causal inference, experimental design, diagnostic analytics, and statistical modeling with ad-level observational data.

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What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at TikTok.

What role is this question designed for?

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Investigate visit–report correlation causality

Q: Investigate visit–report correlation causality

This question evaluates a candidate's competency in causal inference, experimental design, diagnostic analytics, and statistical modeling with ad-level observational data.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at TikTok.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at TikTok during technical interviews.

Causal Diagnosis: Do More Ad Page Visits Cause More Reports?

Context

You observe a positive correlation between the number of ad page visits and the probability that an ad is reported as "bad." Determine whether this is a causal exposure effect (more visits cause more reports) or is driven by selection/exposure confounding (e.g., certain traffic sources, targeting, or instrumentation issues).

Assume you have ad–day–geo–hour level data with counts of visits and reports, plus metadata (ad ID, traffic source, geo, device, time, user-level frequency where available).

Deliverables

Hypotheses: Provide at least three concrete, falsifiable hypotheses (e.g., pure exposure effect, targeting bias to sensitive cohorts, low-quality ads drive both attention and reporting, instrumentation effects). For each, state a falsifiable prediction.
Diagnostic plan: Specify exact metrics and cuts, such as per-visit report rate vs. visit volume buckets; within-ad fixed-effects trends; cohorting by traffic source; funnel-based conditional probabilities.
Statistical model: Propose an ad–day model (logistic or Poisson/negative binomial) that controls for exposure via an offset log(visits), includes ad fixed effects and hour-of-day/geo controls. Explain coefficient interpretation and how you will check overdispersion and multicollinearity.
Quasi-experimental approach: Propose at least one (e.g., holdout geos, randomized delivery caps, or an IV using exogenous traffic shocks like site outages) and describe how you’d run a parallel A/A to validate.
Criteria for action: Define thresholds for an operational alert (e.g., ≥20% lift in per-visit report rate with p<0.01 after multiple-testing control) and recommend product/policy levers under each outcome.

Context

Assume you have ad–day–geo–hour level data with counts of visits and reports, plus metadata (ad ID, traffic source, geo, device, time, user-level frequency where available).

Deliverables

Hypotheses: Provide at least three concrete, falsifiable hypotheses (e.g., pure exposure effect, targeting bias to sensitive cohorts, low-quality ads drive both attention and reporting, instrumentation effects). For each, state a falsifiable prediction.

Diagnostic plan: Specify exact metrics and cuts, such as per-visit report rate vs. visit volume buckets; within-ad fixed-effects trends; cohorting by traffic source; funnel-based conditional probabilities.

Statistical model: Propose an ad–day model (logistic or Poisson/negative binomial) that controls for exposure via an offset log(visits), includes ad fixed effects and hour-of-day/geo controls. Explain coefficient interpretation and how you will check overdispersion and multicollinearity.

Quasi-experimental approach: Propose at least one (e.g., holdout geos, randomized delivery caps, or an IV using exogenous traffic shocks like site outages) and describe how you’d run a parallel A/A to validate.

Criteria for action: Define thresholds for an operational alert (e.g., ≥20% lift in per-visit report rate with p<0.01 after multiple-testing control) and recommend product/policy levers under each outcome.

Investigate visit–report correlation causality

Quick Overview

Causal Diagnosis: Do More Ad Page Visits Cause More Reports?

Context

Deliverables

Solution

Comments (0)

Investigate visit–report correlation causality

Quick Overview

Causal Diagnosis: Do More Ad Page Visits Cause More Reports?

Context

Deliverables

Solution

Comments (0)