#Experimentation

Articles about Experimentation — exploring patterns, best practices, and real-world implementations in production systems.

2 posts tagged with experimentation. ← All posts

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Pratik Dhanave · ·6 min read

Randomized Experiments — The Gold Standard, and Your A/B Test

The most reliable way to establish causation is almost embarrassingly simple: flip a coin to decide who gets the treatment. Randomization is the one technique that defeats confounding completely — including confounders you never thought of — which is why the randomized controlled trial is the gold standard of causal evidence and why the A/B test is the most powerful tool an engineer has. This post explains why randomization works its magic, and the many ways real experiments still go wrong.

The most reliable way to establish causation is almost embarrassingly simple: randomize. Why randomization defeats all confounders at once (even unknown ones), why the A/B test is a genuine causal tool, and the many ways real experiments still go wrong (peeking, power, interference, broken randomization).

#Causal Inference#A/B Testing#Experimentation#Randomization#Data Science#Statistics#Testing
Pratik Dhanave · ·6 min read

Correlation Is Not Causation — Why Engineers Keep Getting This Wrong

Every data-driven engineer knows the phrase "correlation is not causation," and almost everyone violates it anyway — in A/B test readouts, in metric dashboards, in ML models used to make decisions. The gap between "these move together" and "this causes that" is where enormous amounts of money and effort get wasted on changes that don't work. Causal inference is the discipline of crossing that gap rigorously, and this series builds it from first principles for people who build systems.

Everyone knows 'correlation is not causation' and violates it anyway — in A/B readouts, dashboards, and ML used to drive decisions. The real difference (observing vs intervening), the three reasons correlation appears without causation (reverse causation, confounding, selection), and why it's an engineering problem.

#Causal Inference#Data Science#Experimentation#Machine Learning#Statistics

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.