Researcher-Practitioner's Toolkit

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.

I’ve seen it countless times, from agile startups to corporate giants. A business leader asks for a simple, weekly report—say, on project profitability. The tech team, brilliant and eager to use the latest tools, returns three months later with a real-time, Kafka-powered, microservice-architected, auto-scaling data pipeline. The cost? Astronomical. The complexity? A nightmare to maintain. The business value? The exact same as the simple, static report the leader originally ne

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.

I've been in the trenches of sales and marketing for decades, leading teams at giants like Itaú, Stone, and Alura. I’ve seen the sheer...

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...