Prove source growth is cannibalization, not incremental

Q: Prove source growth is cannibalization, not incremental

This question evaluates causal inference and experimental-design competencies in analytics, testing skills in identification strategy, treatment and control definition, specification of difference‑in‑differences or randomized geo experiments, metric construction, power calculations, and robustness checks for attribution and cannibalization analysis. It is commonly asked to determine whether observed growth is incremental or substitution-driven, falls under the Analytics & Experimentation domain in Data Science, and emphasizes practical application accompanied by conceptual understanding of identification assumptions, statistical power, and measurement.

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 Onsite rounds at Meta.

Q: What role is this question designed for?

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

Question

Causal Analysis Design: Is Web Growth Incremental or Cannibalization?

Background

You observe that revenue attributed to creation_source = "web" is higher in 2026 compared to 2025. You need to determine whether this increase is primarily incremental or reflects cannibalization/substitution from other acquisition sources (api, mobile).

Assume revenue is tracked weekly by geo and source, and budgets can be adjusted by source at the geo level.

Task

Design a causal analysis that tests whether web growth is primarily cannibalization rather than incremental revenue. Address the following:

Identification

Choose and justify an approach (e.g., randomized geo budget shift, difference-in-differences with matched geos, or synthetic control).
Define treatment (e.g., reducing non-web budgets by X% while holding web constant, or vice versa), control units, and the time windows (pre, treatment, washout).

Model

Specify the core difference-in-differences (DID) equation with unit and time fixed effects and an interaction capturing treatment, plus controls for seasonality, macro trends, advertiser mix, and product changes.
State key assumptions (parallel trends, no spillovers) and how you’ll test pre-trends and interference (e.g., cluster or partial interference models).

Metrics

Define incremental revenue and the substitution/cannibalization rate = −ΔRevenue_other_sources / +ΔRevenue_web within randomized units.
Specify confidence intervals, unit of analysis (geo, advertiser, cohort), and aggregation.

Power

Provide a minimal detectable effect (MDE) calculation given historical variance and sample size (number of geos/advertisers and weeks). A worked example is acceptable.

Robustness

Plan placebo tests on pre-periods, negative-control outcomes, alternative specifications (event study, synthetic control), and sensitivity to heterogeneous treatment effects.

Decision Rule

Pre-specify thresholds on cannibalization rate and incremental ROI that would trigger budget reallocation.
Provide a mocked analysis-plan table schema you’d need (unit_id, date, source, revenue, spend, cohort, geo, treatment_flag).

Prove source growth is cannibalization, not incremental

Quick Overview

Causal Analysis Design: Is Web Growth Incremental or Cannibalization?

Background

Task

Solution

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