Product Experimentation for Collaborative AI Features: Cluster Randomization for LLM-Based Tools in Python

                     Product Experimentation for Collaborative AI Features: Cluster Randomization for LLM-Based Tools in Python
               



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                     Product Experimentation for Collaborative AI Features: Cluster Randomization for LLM-Based Tools in Python
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

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Every product experimentation team running causal inference on LLM-based collaborative features eventually hits the same wall: your users aren't independent. Your team ships an AI meeting summarizer to half the enterprise accounts on your platform. The rollout's clean, half on and half off, and you wait for the control group's task completion to stay flat while the treated group's creeps up. Two weeks in, the control group's numbers are moving too. Not as much, but visibly. The feature's confirmed off for those accounts, and you've checked the rollout config twice. Something's still contaminating your control.

You know what it is before you dig into the logs. The AI meeting summaries land in shared Slack channels, the AI-drafted docs show up in shared Google Drive folders, and the AI code review suggestions appear in pull requests that both treated and control engineers read. Behavior changes for the treated users, and a slice of that behavior bleeds back into your control group through the collaboration graph.

This is the collaborator contamination trap. It shows up in every generative AI product that touches shared artifacts: AI meeting notes that teammates read, AI-drafted documents that coworkers edit, AI code suggestions that reviewers evaluate, AI-generated email threads that the whole team replies to. User-level randomization assumes one user's treatment assignment leaves every other user's outcome alone. In a collaborative workspace, that assumption is wrong by design, and the product experiment folds the feature's real effect together with the spillover it creates inside the control group.

Running a collaborative AI feature behind a user-level A/B test is a product experiment that violates the Stable Unit Treatment Value Assumption (SUTVA). The fix is cluster randomization: flip the coin at the workspace level, so entire teams are in or out together, then model the cross-workspace spillover directly.

This tutorial walks through the full pipeline (cluster assignment, a biased, naive user-level OLS, cluster-weighted least squares for honest standard errors, a two-exposure decomposition that identifies direct and spillover effects separately, and cluster-bootstrap confidence intervals) on a 50,000-user synthetic SaaS dataset in which the ground-truth causal effects are known. You'll estimate them, quantify uncertainty, and see where the approach silently breaks.

Companion code: every code block runs end-to-end in the companion notebook at github.com/RudrenduPaul/product-experimentation-causal-inference-genai-llm/tree/main/05_cluster_randomization. The notebook (

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cluster_randomization_demo.ipynb) has all outputs pre-executed, so you can read along on GitHub before running anything locally.

Table of Contents

• Why user-level A/B randomization breaks under collaboration

• What cluster randomization actually does

• Prerequisites

• Setting up the working example

• Step 1: Build the cluster assignment and spillover exposure

• Step 2: Naive user-level OLS (biased and overconfident)

• Step 3: Cluster-weighted least squares (honest standard error)

• Step 4: Two-exposure decomposition (unbiased direct and spillover)

• Step 5: Cluster-bootstrap confidence intervals

• When cluster randomization fails

• What to do next

Why User-Level A/B Randomization Breaks Under Collaboration

The math of an A/B test is elegant because one user's treatment assignment has no bea

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