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Design experiment with network and novelty effects

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competence in experimental design and causal inference under network interference and novelty effects, covering skills such as choice of randomization unit, estimand specification, exposure mapping, and bias-aware inference for spillovers and temporal heterogeneity.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Design experiment with network and novelty effects

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

Design an experiment to measure the impact of a new calling feature while addressing both novelty effects and network interference. Describe: (a) the randomization unit you would choose (user, pair/edge, or cluster/ego-network) and why; (b) how you'd handle treatment callers contacting control users (two-stage randomization or cluster holdouts); (c) what happens to control-group outcomes under interference and how you would estimate the total effect vs. direct effect; (d) how you'd detect and mitigate novelty effects over time (e.g., ramp schedules or CUPED + temporal heterogeneity modeling); and (e) your analysis plan if some control users receive spillovers. Include diagrams/notation as needed and specify estimands.

Quick Answer: This question evaluates a data scientist's competence in experimental design and causal inference under network interference and novelty effects, covering skills such as choice of randomization unit, estimand specification, exposure mapping, and bias-aware inference for spillovers and temporal heterogeneity.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Analytics & Experimentation
2
0
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Experiment Design: New Calling Feature with Network Interference and Novelty Effects

Context: You are launching a new calling feature on a large social platform where calls occur along a known social graph G = (V, E). A user's outcome (e.g., calls placed, calls received, call minutes, call acceptance rate) may be affected by both their own assignment and their neighbors' assignments (network interference). The feature is also expected to have strong novelty effects (initial excitement/learning curves).

Task: Design an experiment and analysis plan that measures product impact while addressing both interference and novelty. Specifically describe:

  1. Choice of randomization unit
    • Choose among: user, pair/edge, or cluster/ego-network. Justify the choice, including trade-offs.
  2. Handling treated callers contacting control users
    • Propose either a two-stage randomization (cluster-level coverage + within-cluster user assignment) or cluster/ego-network holdouts. Specify how calls between treated–control pairs are handled.
  3. Interference and estimands
    • Explain what happens to control-group outcomes under interference.
    • Define and specify estimands for the total effect vs. direct effect (and indirect/spillover, if used). Include notation.
  4. Detecting and mitigating novelty effects
    • Describe ramp schedules and how you would detect novelty over time.
    • Describe analytical adjustments (e.g., CUPED, event-study/temporal heterogeneity modeling) to isolate steady-state effects.
  5. Analysis plan with spillovers into control
    • If some control users receive spillovers, describe how you will estimate effects and uncertainty without bias (e.g., exposure mapping, IPW/HT estimators, IV, randomization inference).

Include simple diagrams/notation as helpful and be explicit about assumptions and estimands.

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