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...

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...

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

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

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.

By implementing methods rooted in selection on observables, HR departments can move from celebrating misleading correlations to confidently identifying programs that genuinely increase employee productivity, decrease costly turnover, and foster a truly high-performing culture. This is how you transform HR from a cost center into a strategic driver of value.

Let's cut to the chase. In the world of data, we're constantly bombarded with "insights" that promise to revolutionize our business. But...