Jun 5, 202611 minS2
<p>EP4: Two Ways to Measure Demand, and When the Market Lens Matters</p><p>"Demand" can mean more than one thing. In day-to-day product analytics, it’s the chart right in front of us: sessions, searches, transactions...
About this Podcast:
Inference & Intelligence Lab is a podcast on statistical inference, causal inference, machine learning, and GenAI evaluation, focused on making decisions that hold up in real-world data science. The show features two series—Causal Inference From the Ground Up and Inference in the Wild—covering both first principles and practical pitfalls.
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Contact path
Public web form
Booking probability
27%
Guest openness
Selective
80/100
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What is Inference & Intelligence Lab about?
Inference & Intelligence Lab is a podcast on statistical inference, causal inference, machine learning, and GenAI evaluation, focused on making decisions that hold up in real-world data science. The show features two series, Causal Inference From the Ground Up and Inference in the Wild, covering both first principles and practical pitfalls.
Who hosts Inference & Intelligence Lab?
Inference & Intelligence Lab is hosted by Lin Jia. The show has published 11 episodes to date.
What has Inference & Intelligence Lab covered recently?
Recent episodes include "Two Ways to Measure Demand, and When the Market Lens Matters | EP4: Inference in the Wild" (5 June 2026, 11 minutes), "The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces" (22 May 2026, 21 minutes), and "Build the Camera, How Measurement Design Guides Statistical Testing | EP2: Inference in the Wild" (10 April 2026, 8 minutes).
How often does Inference & Intelligence Lab publish?
Inference & Intelligence Lab publishes roughly every two weeks, with episodes averaging 15 minutes across 11 episodes.
How hard is it to get booked on Inference & Intelligence Lab?
Booking probability for Inference & Intelligence Lab is 27%, and the host is rated Selective on guests. Inference & Intelligence Lab requires a Pod Score of 80 or higher. Inference & Intelligence Lab's contact route on record: public web form.
Who should pitch Inference & Intelligence Lab?
The strongest fits for Inference & Intelligence Lab are Scientific Research & Development and IT Services & Software Development. Inference & Intelligence Lab has a technology audience.
About the host
Lin Jia hosts Inference & Intelligence Lab, a technology podcast with 11 episodes published.
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Recent episodes
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Episode #3
The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces
May 22, 202621 minS2<p>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...
Episode #2
Build the Camera — How Measurement Design Guides Statistical Testing | EP2: Inference in the Wild
Apr 10, 20268 minS2<p>EP2: Build the Camera — Why Measurement Design Trumps Statistical Testing</p><p>Running a statistical test is simply pressing the shutter. But designing the measurement system? That is building the camera.</p><p>In...
Episode #7
No Interference, No Ambiguity: The SUTVA Assumption ｜ EP7: Causal Inference from the Ground Up
Apr 3, 202613 minS1<p>No Interference, No Ambiguity: The SUTVA Assumption</p><p>Your randomized experiment is clean. The groups are balanced and comparable. The p-value is significant. But behind the scenes, the treatment is leaking. User...
Episode #6
No Overlap, No Answer: The Positivity Assumption | Ep6: Causal Inference from the Ground Up
Mar 27, 202614 minS1<p>No Overlap, No Answer: The Positivity Assumption</p><p>A causal effect can only be estimated where a comparison is actually possible. Imagine evaluating a loyalty program where every enterprise customer is already...
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Mar 15, 202621 minS1<p><b>Comparing Apples to Apples: The Exchangeability Assumption</b></p><p>Your dashboard flags a troubling trend: users who contacted customer support have a 40% higher churn rate than those who didn’t. The immediate...
Mar 1, 202615 minS1<p>You can never see the data you need most to make a decision. 📉</p><p>It sounds counterintuitive, but the core of Causal Inference isn't just math—it's imagination. 🌌</p><p>Most Data Science focuses on predicting...
Feb 19, 202616 minS1<p>Headline: Why your "Perfect" Models are failing the Boardroom.</p><p>For months, the team worked on the model. The AUC was 0.92. The validation sets were clean. But when you shipped it to the real world? Nothing...
Feb 8, 202612 minS1<p>The Infinite Data Trap: Why More Data Won't Save Your Causal Models</p><p>You have petabytes of user data. Your model has 99% validation accuracy. But when you ask it, "What happens if we change our strategy?", it...
Feb 1, 202618 minS2<p>The Ranking Trap: Why PLR Fails with Multiple Treatments</p><p>You’re testing four different promotional strategies—a discount, free shipping, BOGO, and loyalty points. You run a Partially Linear Regression (PLR)...
Jan 12, 202613 minS1<p>The Recipe for Causal Truth: Estimand, Estimator, and Estimate</p><p>You have a dataset, a model, and a final number. But can you explain—with precision—what that number actually represents?</p><p>In the world of...
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