Causal Experimentation 101: Prove Impact Without an A/B Test

Part of AI Analytics Frameworks

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Sep 16, 2026

5:00 PM UTC (45 minutes)

Virtual (Zoom)

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Automate AI Evals with Claude Code
Shane Butler
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What you'll learn

Use pre-post, diff-in-diff, and matching

The three go-to methods for estimating impact when a clean randomized test was never an option.

Know which method fits your situation

A simple decision path from what happened and what data you have to the method that will actually hold up.

Spot the confounders that fool you

The seasonality, selection, and trend traps that make a change look like it worked when it did not.

Make a defensible causal claim

State the effect, the assumptions behind it, and the caveats, so it survives the first hard question.

Why this topic matters

Most impactful questions never get a clean A/B test. The feature shipped, the change hit everyone at once, or the sample is too small. Pre-post, diff-in-diff, and matching let you estimate real impact from your data, and AI makes running them fast. The hard part is picking the right method and catching the confounders that break your claim. This is the no-experiment causal toolbox, made simple.

You'll learn from

Shane Butler

Co-founder, AI Analyst Lab

Shane Butler is a Co-Founder of the AI Analyst Lab. Previously he led evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

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