Inference & Intelligence Lab
Lin JiaInference & Intelligence Lab covers statistical inference, causal inference, ML, and GenAI evaluation for trustworthy decisions. Two series: Causal Inference From the Ground Up and Inference in the Wild.
| Platform |  | Pricing | Only free issues | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 23 | Founded | 5 months ago | Last Issue | 7 days ago |
| Active | |||||
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inferenceintel.substack.comLatest Issues
Two Ways to Measure Demand, and When the Market Lens Matters
📊 "Demand" can mean more than one thing.
In day-to-day analysis, it's the chart in front of us: sessions, searches, transactions, conversion rates.
These are real, valuable numbers, and strong teams track them closely. They capture one l...
GenAI in Experimentation: Notes from the Panel and the Sidelines
🎤 The GenAI & Experimentation panel at Booking.com's 3rd Experimentation Conference covered roughly four questions in 20 minutes — I was invited to join the panel discussion alongside David Gregory (Skyscanner), Marcel Toben (Zalando), and...
The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces
A feature can create value and still fail the experiment.
In marketplaces, many features depend on users or partners choosing to opt in — loyalty programs, vouchers and incentives, partner optimization tools, email messaging. That creates...
The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces
Across the tech industry, many of the most valuable features rely on voluntary adoption. A traveler chooses whether to join a loyalty program, or a marketplace seller decides whether to opt into a smart-pricing tool. Because you cannot forc...
GenAI Literacy Isn't Usage
This is the first Field Note — a new short-form series running alongside Inference in the Wild and Causal Inference from the Ground Up. These are reflections from the day-to-day of working as a senior data scientist: the small judgment call...
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From causal inference foundations to production-grade intelligence—solving the messy problems of modern Data Science.
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