You test a label that warns users before resharing a suspected fake-news link. Because exposure and behavior propagate on the graph, SUTVA is violated. Design an experiment that identifies total, direct, and spillover effects using cluster randomization: (a) how you construct clusters (e.g., graph partitioning to minimize cross-cluster edges), (b) exposure mapping to define treated vs indirectly-exposed users, (c) primary outcomes (reshare rate, unique reach) and misinfo prevalence reduction, (d) estimation strategy (Horvitz–Thompson or difference-in-means with cluster weights) and power implications of cluster size, and (e) sensitivity analyses for contamination. How will you detect strategic adversarial adaptation during the test?

This question evaluates a data scientist's competency in causal inference and experiment design under network interference, covering cluster-randomized trials, exposure mapping, estimation of total, direct and spillover effects, power analysis, robustness checks, and detection of adversarial adaptation.

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What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

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

Measure fake-news interventions under network interference

Experiment Design Under Interference: Warning Label for Suspected Fake-News Reshares

Context

You are testing a pre-reshare warning label for links suspected to be misinformation. Because both exposure and behavior propagate along the social graph (e.g., seeing friends' reshares, feed ranking, social reinforcement), the Stable Unit Treatment Value Assumption (SUTVA) is violated: one user's treatment can affect others.

Design a cluster-randomized experiment that can identify total, direct, and spillover (indirect) effects despite interference.

Task

Describe a design that includes:

(a) Cluster construction

How will you partition the graph into clusters to minimize cross-cluster interference? Outline practical constraints (e.g., cluster size bounds) and algorithms.

(b) Exposure mapping

Define what counts as a treated user vs an indirectly-exposed user under partial interference. State any thresholds or functions of neighbors' treatment you will use.

Specify primary outcomes, such as reshare rate and unique reach. Include a system-level metric for misinformation prevalence reduction.

(d) Estimation strategy and power

Describe estimators for total, direct, and spillover effects (e.g., Horvitz–Thompson or difference-in-means with cluster weights). Discuss how cluster size and intra-cluster correlation influence power.

(e) Sensitivity analyses

Propose checks for contamination (cross-cluster edges) and robustness to exposure-mapping choices.

Finally, explain how you will detect strategic adversarial adaptation (e.g., attempts to circumvent the label) during the test.

Experiment Design Under Interference: Warning Label for Suspected Fake-News Reshares

Context

Design a cluster-randomized experiment that can identify total, direct, and spillover (indirect) effects despite interference.

Task

Describe a design that includes:

(a) Cluster construction

How will you partition the graph into clusters to minimize cross-cluster interference? Outline practical constraints (e.g., cluster size bounds) and algorithms.

(b) Exposure mapping

Define what counts as a treated user vs an indirectly-exposed user under partial interference. State any thresholds or functions of neighbors' treatment you will use.

Specify primary outcomes, such as reshare rate and unique reach. Include a system-level metric for misinformation prevalence reduction.

(d) Estimation strategy and power

Describe estimators for total, direct, and spillover effects (e.g., Horvitz–Thompson or difference-in-means with cluster weights). Discuss how cluster size and intra-cluster correlation influence power.

(e) Sensitivity analyses

Propose checks for contamination (cross-cluster edges) and robustness to exposure-mapping choices.

Finally, explain how you will detect strategic adversarial adaptation (e.g., attempts to circumvent the label) during the test.

Measure fake-news interventions under network interference

Quick Overview

Experiment Design Under Interference: Warning Label for Suspected Fake-News Reshares

Context

Task

Solution

Submit Your Answer to Earn 20XP

Measure fake-news interventions under network interference

Quick Overview

Experiment Design Under Interference: Warning Label for Suspected Fake-News Reshares

Context

Task

Solution

Submit Your Answer to Earn 20XP