Researcher-Practitioner's Toolkit

You launched a bold new sales incentive program in specific cities, but not in their surrounding suburbs. A quarter later, sales in the...

Your team just ran a Regression Discontinuity Design (RDD) to measure the impact of your new scholarship program. The result is......

Your 'Gold Tier' loyalty program is full of high-spending customers. Did the program make them high-spenders, or did you just give perks...

Over the last few weeks, we've assembled a powerful toolkit for causal inference from observational data: Covariate Adjustment,...

To measure the ROI of your new leadership training, you controlled for employee morale. It seems smart. It seems rigorous. But what if I...

Your team needs to know if the new digital invoicing system is truly making them more efficient. You have two standard ways to measure...

Your ad campaign "lifted" sales by 10%. Are you sure it was the campaign? Or was it just... November? This is one of the most common...

You’re trying to measure the impact of a new sales strategy. You’ve dutifully controlled for every variable you can think of—rep...

That voluntary leadership program you launched... did it actually create future leaders, or did it just attract the ones you already...

You just surveyed your customers, and they love your new sustainable product idea. 90% say they are willing to pay a premium for it....

For the past two months, we've gone deep. We've talked Causal DAGs, RCTs, SUTVA, and the failures of strategy that lead to "dashboard...

In any good consulting project, there comes a moment to pause, review the initial findings with the client, and chart the course for...

Your regression model is beautiful. But are you sure you should be "controlling for" that variable? This is one of the most subtle—and...

To every data scientist, ML engineer, and BI analyst: Your model has a 98% accuracy score. The code is beautiful. The architecture is...

I just came out of a strategy meeting, and I have to say this out loud. We are so obsessed with advanced analytics, AI, and machine...

A consulting team advises a retail client to test a new in-store display. They roll out the new display in their 10 best-performing urban stores and compare sales to the 10 worst-performing rural stores. They run the "test" for one week. The result? A beautiful PowerPoint deck showing a 50% lift, leading to a multi-million dollar national rollout. This is analytical malpractice. The two groups of stores were never comparable (selection bias), and a single week of data is mean

Does that 20% discount coupon actually make you money, or is it just an expensive gift to customers who would have bought from you anyway? Answering this question is fundamental to your marketing ROI. And most companies get it dangerously wrong. This is a classic "Doing Data Wrong" scenario I see in Marketing Departments all the time. The Wrong Way (Observational Illusion): A team analyzes historical data. They see that customers who used a promotional coupon have a 30

This is the most classic form of "Kindergarten Data": you ask for a tool without defining the problem. The result? A "dashboard graveyard." Your team spends weeks building a beautiful, complex dashboard with 50 filters. You look at it, get overwhelmed by charts and numbers that don't lead to a clear decision, and after two weeks, you stop opening it. The data team is frustrated. You’re disappointed. And a massive amount of time and money has been wasted creating a useless

we have to think like physicists. We must start with the Potential Outcomes Framework. It’s a simple but profound idea: for any person, city, or store, there are multiple potential realities. There’s the reality where they got your new marketing campaign, and an alternate, counterfactual reality where they didn't. The "causal effect" is the difference between these two states. The fundamental problem is that we can only ever observe one of these realities at a time.

Here are the types of questions I live to answer: “Our dashboard shows that when sales reps make more calls, customer churn increases. Are the calls driving them away?” (A classic confounding question!) “We launched a new wellness program, and employee productivity went up. How do I prove the program caused the improvement, and it wasn't just a good quarter?” "What is the single most dangerous, 'looks-right-but-is-wrong' metric you've ever seen a C-level executive