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RPRudrendu Paulfreecodecamp.org2d ago · 22 min read

Product Experiment Counterfactual Methods for Estimating the Effects of AI Prompt Engineering

Imagine your team deployed Prompt A globally two weeks ago. Tight deadlines and high confidence meant the rollout hit 100 percent of users without any A/B testing, shadow traffic, or holdout groups. W

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RPRudrendu Paulfreecodecamp.orgJul 15 · 20 min read

Product Experimentation with Regression-Based Causal Inference: Estimating LLM Feature Impact with Python and statsmodels

A randomized A/B test is the cleanest form of product experiment available. The coin flip that splits users between the new prompt template and the control removes every possible confounder by constru

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RPRudrendu Paulfreecodecamp.orgJul 9 · 24 min read

Product Experimentation with Uplift Modeling: Targeting Your LLM Feature Rollout to Users Who Actually Benefit (Python Implementation)

Your LLM product experiment just came back positive, with a promising 8-percentage-point lift in task completion. You ship the feature and leadership celebrates. Three months later, the core metric ha

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RPRudrendu Paulfreecodecamp.orgJul 2 · 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.orgJun 30 · 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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CCausalWorldcausalworldmodel.comMay 14 · 4 min read

Causal-JEPA: A New Causal World Model for AI Agents

World models are internal simulations that let AI reason and plan about the environment [1, 2]. To work well, such models must understand objects and their interactions, not just pixels [1, 2]. Causal

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CCausalWorldcausalworldmodel.comMay 14 · 6 min read

LeWorldModel: Why AI Needs to Understand Physics to Reach Human-Level Intelligence

Large Language Models (LLMs) like OpenAI GPT systems or models from Google DeepMind and Meta have shown incredible capabilities in language, coding, reasoning, and content generation. Yet, despite the

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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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#causal-inference

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Trending tags this week

Product Experiment Counterfactual Methods for Estimating the Effects of AI Prompt Engineering

Product Experimentation with Regression-Based Causal Inference: Estimating LLM Feature Impact with Python and statsmodels

Product Experimentation with Uplift Modeling: Targeting Your LLM Feature Rollout to Users Who Actually Benefit (Python Implementation)

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

Causal-JEPA: A New Causal World Model for AI Agents

LeWorldModel: Why AI Needs to Understand Physics to Reach Human-Level Intelligence

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