Your ML model predicts perfectly but recommends wrong actions. That is not a paradox; it is a fundamental limitation of pattern recognition when it is mistaken for decision-making. "Causal Inference Is Eating Machine Learning" makes a crucial point: prediction accuracy and actionability are separate things. A model that nails every forecast can still lead you to the wrong business move because it learned correlations, not causes.
This matters for anyone building data workflows today. If you rely on a model to tell you what will happen, but not why, you are flying blind when you try to intervene. A practical remedy exists: a five-question diagnostic to check whether your model confuses correlation with causation, plus a method comparison matrix and a Python workflow. That is exactly the kind of grounded guidance that transforms a theoretical insight into something you can use tomorrow. For spreadsheet users, and that includes most of us who manage data day to day, the takeaway is clear. Traditional tools and simple ML models treat every column as equally important. Causal inference forces you to ask which variables actually drive outcomes. That is the difference between knowing that sales spike when you email customers and knowing that emailing them *caused* the spike.
We see this as a natural evolution, not a disruption. Spreadsheet technology has always been about making sense of numbers. The next step is making sense of why those numbers move. You do not need to become a statistician to do this. You need a structured approach, the diagnostic questions, the comparison matrix, the workflow, to separate signal from noise. That is accessible. That is actionable.
The concrete point is this: stop optimizing for prediction accuracy alone. Start building models that answer "what happens if I change this variable?" Your spreadsheet already lets you ask "what if." Causal inference lets your machine learning do the same thing, at scale, with rigor. The rest is up to you. The rest is up to you.