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RPRudrendu Paulfreecodecamp.org1d ago · 21 min read

Product Experimentation: Stop Early Without P-Hacking Using mSPRT and Sequential Testing in Python

Your AI product experiment reaches statistical significance on day 14 of a planned 30-day run, measuring a causal inference question: did the LLM-based feature genuinely improve outcomes? Every produc

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RPRudrendu Paulfreecodecamp.org3d ago · 28 min read

Product Experimentation for LLM Platforms: Switchback Designs When User Randomization Breaks Market Equilibrium in Python

Your team ships an intelligent query-routing feature for an LLM SaaS platform. The feature reads each incoming request in real time and decides whether to send it to the fast standard model or the mor

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RPRudrendu Paulfreecodecamp.orgMay 12 · 21 min read

Product Experimentation with Synthetic Control: Causal Inference for Global LLM Rollouts in Python

Every product experimentation team doing causal inference on LLM-based features eventually hits the same wall: when the provider ships a new model version, there's no holdout. Your infrastructure team

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RPRudrendu Paulfreecodecamp.orgMay 8 · 20 min read

Product Experimentation with Regression Discontinuity: How an LLM Confidence Threshold Creates a Natural Experiment in Python

Causal inference for LLM-based features starts with one question editors ask before they ship anything: Did the change actually move the metric, or did the metric just move? Let's say that your team b

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RPRudrendu Paulfreecodecamp.orgApr 30 · 20 min read

Product Experimentation with Propensity Scores: Causal Inference for LLM-Based Features in Python

Every product experimentation team running causal inference on LLM-based features eventually hits the same wall: when users click "Try our AI assistant," the volunteers aren't a random sample. Your pr

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RPRudrendu Paulfreecodecamp.orgApr 22 · 20 min read

Product Experimentation for AI Rollouts: Why A/B Testing Breaks and How Difference-in-Differences in Python Fixes It

Your team shipped an LLM-based summaries feature to wave 1 workspaces at week 20 and now the post-launch doc is due. You need a causal effect number, a specific estimate you can defend to a statistici

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#product-experimentation

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Product Experimentation: Stop Early Without P-Hacking Using mSPRT and Sequential Testing in Python

Product Experimentation for LLM Platforms: Switchback Designs When User Randomization Breaks Market Equilibrium in Python

Product Experimentation with Synthetic Control: Causal Inference for Global LLM Rollouts in Python

Product Experimentation with Regression Discontinuity: How an LLM Confidence Threshold Creates a Natural Experiment in Python

Product Experimentation with Propensity Scores: Causal Inference for LLM-Based Features in Python

Product Experimentation for AI Rollouts: Why A/B Testing Breaks and How Difference-in-Differences in Python Fixes It

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