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Causal Bandits Podcast
Similar topics: causal and inference
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Inference & Intelligence Lab
by Lin Jia
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Latest episodes
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Two Ways to Measure Demand, and When the Market Lens Matters | EP4: Inference in the Wild
June 5, 2026 · 11 minEP4: Two Ways to Measure Demand, and When the Market Lens Matters "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, and conversion rates. This is…
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The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces
May 22, 2026 · 21 minAcross 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…
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Build the Camera — How Measurement Design Guides Statistical Testing | EP2: Inference in the Wild
April 10, 2026 · 8 minEP2: 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…
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No Interference, No Ambiguity: The SUTVA Assumption ｜ EP7: Causal Inference from the Ground Up
April 3, 2026 · 13 minNo Interference, No Ambiguity: The SUTVA Assumption 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 A shared their referral link…
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No Overlap, No Answer: The Positivity Assumption | Ep6: Causal Inference from the Ground Up
March 27, 2026 · 14 minNo Overlap, No Answer: The Positivity Assumption A causal effect can only be estimated where a comparison is actually possible. Imagine evaluating a loyalty program where every enterprise customer is already enrolled—leaving you with no…
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Comparing Apples to Apples: The Exchangeability Assumption | EP5: Causal Inference from the Ground Up
March 15, 2026 · 21 minComparing Apples to Apples: The Exchangeability Assumption Your dashboard flags a troubling trend: users who contacted customer support have a 40% higher churn rate than those who didn’t. The immediate takeaway seems obvious—support is…
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The Data You'll Never See: Understanding Potential Outcomes | Causal Inference from the Ground up EP4
March 1, 2026 · 15 minYou can never see the data you need most to make a decision. 📉 It sounds counterintuitive, but the core of Causal Inference isn't just math—it's imagination. 🌌 Most Data Science focuses on predicting the future based on what happened in…
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Ladder of Causation: How to Upgrade from Prediction to Policy | Causal Inference from the Ground up EP3
February 19, 2026 · 16 minHeadline: Why your "Perfect" Models are failing the Boardroom. 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 happened. The metrics didn't…
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The Bridge to Truth: Why Identification Comes Before Estimation | Causal Inference from the Ground up EP2
February 8, 2026 · 12 minThe Infinite Data Trap: Why More Data Won't Save Your Causal Models 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 gives you an answer that is…
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The DoubleML Ranking Disaster: Why PLR Fails for Multiple Discrete Treatment | Inference in the Wild EP1
February 1, 2026 · 18 minThe Ranking Trap: Why PLR Fails with Multiple Treatments You’re testing four different promotional strategies—a discount, free shipping, BOGO, and loyalty points. You run a Partially Linear Regression (PLR), get a clean table of…
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The Core Trio of Causal Inference & The Art of Baking a Causal Cake | Causal Inference from the Ground up EP1
January 12, 2026 · 13 minThe Recipe for Causal Truth: Estimand, Estimator, and Estimate You have a dataset, a model, and a final number. But can you explain—with precision—what that number actually represents? In the world of causal inference, precision is…
About the show
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.