Research to ROI | Beyond A/B Tests: A Causal Method to Finally Measure the True ROI of Your Training Programs

You just spent $2 million on a new sales training program. The team that took the course showed a 15% lift in performance six months later. Time to celebrate and roll it out globally, right?

Not so fast. You might have just spent $2 million to prove that your most motivated salespeople are... motivated.

This is one of the most common and costly ways of "doing data wrong." A company offers an optional program. They later compare the performance of those who participated (the "treated") to those who didn't (the "control"). The treated group looks better, and the program is declared a success. The fatal flaw is selection bias. The most ambitious, highest-potential employees are almost always the first to raise their hands for voluntary training. They were likely on an upward trajectory anyway. The company is mistakenly attributing pre-existing drive to the program's effectiveness, potentially wasting millions on an initiative that has zero actual impact.

So how do we find the truth when a real A/B test is impossible? This is where we, as leaders, must demand more rigor. The answer isn't to guess; it's to use a powerful quasi-experimental method, beautifully demonstrated in a new NBER paper by Hyman et al. on worker retraining.

The "Aha!" moment is that you don't need a perfect RCT to get a causal answer; you need to build a credible control group. The method they use is Nearest-Neighbor Matching.

Here’s an analogy: Imagine you want to test a new fertilizer on a specific, unique rose bush in your garden. You can't clone it for a perfect A/B test. But what you can do is meticulously search the rest of the garden for its "statistical twin"—another bush of the same age and species, planted in similar soil, getting the same sunlight. You apply the fertilizer to your target bush but not to its twin. The difference in their growth is now a credible measure of the fertilizer's true, causal impact.

The payoff for applying this rigor is the ability to measure the real ROI. Let's revisit that sales program. Instead of a flawed comparison, the People Analytics team now uses this method.

They take every salesperson who attended the training. For each one, they dive into their HR and performance data to find their "twin" from the pool of non-attendees—someone with a similar sales record, tenure, territory, and performance trajectory before the program began. Now, they compare the subsequent performance of these meticulously matched pairs. The resulting lift—whether it’s 2%, 15%, or even -1%—is the true, causal ROI of the program. You are no longer guessing. You are measuring.

This isn't just for training programs. This applies to wellness initiatives, marketing campaigns, new software rollouts—any situation where you can't enforce a random trial.

For Managers & Business Leaders: Stop accepting correlations as proof of impact. Your job is to demand a believable counterfactual.

• Ask your data teams: "How do we know this isn't just selection bias? What method have you used to construct a credible control group?"

• Challenge them: "Show me the balance table. How similar were the 'treated' and 'control' groups before the program started?"

• Frame the goal: "I don't want to know if the people who took the training did well. I want to know how much better they did than they would have done otherwise."

For my fellow Data & Analytics Professionals: Our value is in delivering truth, not convenient narratives. It's time to move beyond naive comparisons.

• Master quasi-experimental designs. Nearest-neighbor matching, especially with large administrative datasets, is a powerful tool in our arsenal.

• Focus on building the control group. The credibility of our results lives or dies on the quality of our matches.

• We have the tools to isolate causality. Let's use them to drive real, defensible business decisions.

I have built my career on the principle that rigor is the shortest path to value, whether I was leading teams at global companies or launching my own. My purpose now is to bring these powerful, robust methods from the pages of academic journals directly into your strategic planning. This knowledge is not mine to keep; it's our shared toolkit for making smarter decisions.

Stop guessing your impact. Start measuring it.

For more practical frameworks on data-driven decision-making, subscribe to my email list. Or, to discuss how to design more measurable initiatives for your business, let's connect for a 20-minute call.