You look at the dashboard. Sales for your flagship product are flat, but stable. Customer surveys show a respectable 4.2 out of 5...

Let's be brutally honest. Most corporate recommendation engines are stuck in kindergarten. They operate on a laughably simple logic: "You...

Your company just invested in a state-of-the-art AI system. You were promised transparency. You were promised "Explainable AI." When the...

Your ML model is telling you a beautiful story. The problem? It's probably fiction. You have a model that predicts customer churn with...

Stop Asking "Did It Work?" Start Asking "WHO Did It Work For?" I see it constantly. Companies spend millions on a new training program, a...

Every empty seat on an airplane is a perishable good. The moment the cabin door closes, its potential value drops to zero. The central...

What was the ROI of that massive Olympics ad campaign? Or the real impact of your new flagship store? When you make a huge, one-of-a-kind...

You’re spending millions on programs, but can you prove they actually work? I’m not talking about pretty dashboards showing a “before...

Your new booking platform has been live for two years. You know it created a lift at launch. But what's the real ROI now ? Has the effect...

Your new loyalty program launched six months ago. You know the one-month effect on customer spending, but what about the six-month...

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

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...

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

Your marketing ML model is brilliant at predicting who will buy. But does it actually know why? There’s a dangerous gap between prediction and causation, and many companies are falling into it, armed with the most advanced machine learning tools. They're using a Ferrari to drive straight into a wall. This is one of the most insidious forms of “Doing Data Wrong”: using powerful predictive models to make causal decisions, like where to allocate your marketing budget.

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...

For years, I've sat in boardrooms, launched ventures, and scaled businesses – from Itaú and Ambev to Stone and Alura, and my own...