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What’s the Impact? Experimentation and Causal Inference Get the Playbook

Causal Inference

Causal Inference· July 2026 · 5 min read

The Rollout Trap: Why a Positive A/B Test Doesn’t Mean Treat Everyone

A positive average treatment effect does not tell you who should get the change. I simulated 100,000 searchers, trained a T-learner on the experiment data alone, and checked the targeting decision against ground truth I built myself.
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Causal Inference· July 2026 · 5 min read

The Waiting Tax: How a Surrogate Index Answers a 12-Month Question in 4

The metric you care about takes a year to observe, and nobody wants to hold a decision that long. I simulated a subscription business where I knew the true 12-month answer, then checked how early a surrogate index could recover it.
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Causal Inference· May 2026 · 3 min read

The Linear Trap: Why Double Machine Learning Beats Fixed Effects

I built a simulation to stress-test whether Double Machine Learning actually changes decisions compared to fixed effects OLS. It does, and the gap shows up exactly where product experiences stop being linear.
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Causal Inference· March 2026 · 3 min read

The MAPE Illusion in Marketing Mix Modeling

Why a Better Fitting Model Doesn’t Mean Better Attribution In Data Science, predictive fit metrics like Mean Absolute Percentage Error (MAPE) or R² are our compass. It is highly intuitive—and common practice—to assume that…
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Causal Inference· February 2026 · 3 min read

Estimating Amazon Ad Incrementality for Sellers: Combining Switchback Tests with MMM

I don't have access to Amazon Marketing Cloud. Here is how I used a manual switchback experiment and Google Meridian to triangulate the true value of my ad spend, and why the platform number was almost double the truth.
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Causal Inference· February 2025 · 1 min read

Real-world Data Science Problem: Synthetic Controls using Causal Impact — BEWARE

Synthetic control methods are a core technique for data scientists specializing in causal inference methods. One of the most popular packages to estimate synthetic controls is the CausalImpact package by Google. In this real…
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Causal Inference· February 2025 · 1 min read

Data Science Tutorial: The Event Study — A powerful causal inference model

Here’s a short tutorial and example of an Event Study, a popular and flexible causal inference model. Event study models can be used for a range of business problems including estimating: Excess stock price…
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Causal Inference· February 2025 · 1 min read

Google’s Marketing Mix Model: Dramatically improved MMM results from Meridian

In my previous video, I introduced Google’s marketing mix model called Meridian which was just publicly released open source this past week. In that video, I demonstrated how naive usage of prepackaged models can…
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Causal Inference· February 2025 · 1 min read

Google Meridian: Marketing Mix Model can give crazy results if you aren’t careful

I tested Google’s new Marketing Mix Model aka Meridian with simulated data and it demonstrates how dangerous data science models can be in the wrong hands. Video breakdown here: https://youtu.be/wQbk2TWaH50 I simulated data so…
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Causal Inference· February 2025 · 1 min read

Big news for data scientists: Google released its Meridian Marketing Mix Model for FREE

Data scientists regularly have to demonstrate impact: How well does your work justify your current and future compensation? Large companies may have advertising budgets in the hundreds of millions of dollars so that even…
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