Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Correlation Isn't Causation. Now What?
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Machine learning models are designed to predict outcomes. Given the characteristics of a customer, a machine learning model can, for example, predict whether they'll buy a product, click on a link, cancel a service, or claim on their insurance. But what an ML model can't tell you is what causes the customer to do any of these things - and what would happen if an organisation intervened to change their behaviour. This is because ML models are correlation-based. And as most data scientists know, correlation is not equal to causation. To understand what causes a person to act in a certain way, it's necessary to look beyond ML to causal inference. But causal inference and ML aren't competitors. Data scientists don't have to choose one or the other. In fact, ML and causal inference can be combined, in the form of causal AI, to give executives the power to make decisions based on causation, not just correlation, at scale. That said, most organisations are still catching up with recent advances in predictive AI in the form of LLMs, and haven't had time to consider causal AI. And many data scientists are so caught in the predictive mindset instilled during their training, they struggle to think any other way. In the latest episode of Value Driven Data Science, Dr Quentin Gallea, author of The Causal Mindset Handbook, joins me to explore what a causal mindset actually involves and how causal AI brings causal inference and machine learning together to support better decisions than either can alone. You'll discover:
Knowing that correlation isn't causation is just the beginning. This episode is about what comes next. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 131: Building a Causal Mindset in a Predictive World Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
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