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 | Weekly |
|---|---|---|---|---|---|
| Issues | 20 | Founded | 4 months ago | Last Issue | 38 hours ago |
| Active | |||||
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inferenceintel.substack.comLatest Issues
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...
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...
Build the Camera — How Measurement Design Guides Statistical Testing
Running a statistical test is pressing the shutter. Designing a measurement system is building the camera.
In experimentation discussions, I often hear: “Is the metric power-law distributed?” “Does the Central Limit Theorem apply?” “Should...
Build the Camera — How Measurement Design Guides Statistical Testing | EP2: Inference in the Wild
EP2: Build the Camera — Why Measurement Design Trumps Statistical Testing
Running a statistical test is simply pressing the shutter. But designing the measurement system? That is building the camera.
In this episode, we challenge the indu...
No Interference, No Ambiguity: The SUTVA Assumption
Randomization guarantees balanced, comparable groups. It does not guarantee that what you are comparing is well defined.
The two components of SUTVA — no interference and consistency.
Consider an experiment on a referral programme. Users...
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From causal inference foundations to production-grade intelligence—solving the messy problems of modern Data Science.
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