In the ever-evolving world of data science, machine learning (ML) has long been the foundation of predictive analytics.
From recommendation engines to anti-fraud initiatives, ML algorithms have transformed the manner in which we extract value from data. But as companies increasingly want deeper insights, not just what will happen, but why it happens, traditional machine learning is reaching its limitations. Enter causal inference, a game changer that promises to unlock a new frontier for data science.
Harrison Obamwonyi, a pioneering data scientist, has been a vocal proponent of moving past correlation and towards causality. With his inspiration in mind, let’s examine why causal inference is poised to disrupt the space.
The Shortcomings of Correlation
Traditional machine learning is fantastic at detecting patterns and forecasting on the basis of correlations in data. For example, an ML model can accurately forecast customer churn based on past data. However, it usually fails to provide insight into why customers are leaving or what action would stop them from doing so.
Correlation, as they say, is not causation. A model will notify that customers who are emailed with promotional emails have lower chances of churning, but does the email reduce churn, or are they already more engaged? If a business does not understand cause and effect, it can risk wasting resources or misinterpreting results.
From recommendation engines to anti-fraud initiatives, ML algorithms have transformed the manner in which we extract value from data. But as companies increasingly want deeper insights, not just what will happen, but why it happens, traditional machine learning is reaching its limitations.
This is where causal inference enters the picture. While ML perpetuates predictive bias, causal inference attempts to uncover the causal processes that generate outcomes by asking questions like: “What if we do X?” or “Did Y cause Z?” For data scientists like Harrison, this transition from associative to causal thought is more of an intellectual exercise, it’s a practical necessity when it comes to actually getting real-world problems solved.
What Is Causal Inference?
At its core, causal inference is a set of statistical and conceptual tools used to identify and quantify cause-and-effect relationships.
Adopting from areas of study such as epidemiology, economics, and the social sciences, it draws upon mechanisms such as randomized controlled trials (RCTs), propensity score matching, and structural causal models.
Under ideal conditions, RCTs, one receiving an intervention, one not represent the gold standard for identifying causality.
But in most cases, RCTs are unethical or unfeasible. It is here that sophisticated causal methods and observational data enter the equation, allowing data scientists to infer causality from real-world data sets.
For instance, if a store wants to know the impact of a discount campaign on sales, a typical ML algorithm can predict future sales using past trends but will not assign the influence to the campaign itself. Causal inference, however, would use techniques like difference-in-differences or instrumental variables to estimate the true effect after adjusting for confounding factors such as season or demographics of buyers.
While ML perpetuates predictive bias, causal inference attempts to uncover the causal processes that generate outcomes by asking questions like: “What if we do X?” or “Did Y cause Z?”
Why Causal Inference Matters Now
The rise of causal inference in data science is not a fad, it’s a response to the mounting complexity of decision-making in the digital age. Businesses no longer want black-box predictions; they need actionable insights that guide strategy.
Governments and healthcare professionals are also seeking out causal methods to evaluate policies and interventions. Harrison has highlighted this paradigm break, noting that “data science isn’t about predicting the future, it’s about building it.” Causal inference enables practitioners to do just that.
Moreover, the combination of causal inference and machine learning is creating a strong hybrid approach. Techniques like causal forests and double machine learning combine ML’s predictive power with causal nuance so that data scientists can tackle problems previously out of reach. For instance, in medicine, these methods can tell us whether treatment improves patient outcomes, not just predict who will get better.
Challenges and Opportunities
Adopting causal inference is not without difficulty. It implies a shift in thinking from pattern recognition to hypothesis-based reasoning. Data scientists will need to deal with the detection of confounders—variables that affect both the cause and the effect—and ensuring that their models are robust to biases. Causal inference also requires high-quality data and domain knowledge because assumptions regarding causal relationships must be drawn from actual context.
For data scientists like Harrison, this transition from associative to causal thought is more of an intellectual exercise, it’s a practical necessity when it comes to actually getting real-world problems solved.
But with these threats come threats of opportunity. For Harrison-like practitioners, causal inference dominance is a chance to stand out among an ocean of equals. It’s a skill that bridges the gap between data science and decision science and places the practitioners as a strategic partner rather than analysts.
Organizations can also gain through investment in causality capability—opening doors of access to insight to drive not just efficiency but innovation.
The Future of Data Science
As we move beyond legacy machine learning, causal inference will be the foundation of data science. It is not a substitute for ML, but a complement—something that gives us depth and interpretability to our models. To data scientists like Harrison, it’s a chance to rethink how we think about data, to pose not just “What can we predict?” but “What can we change?
In a sea of data, the ability to separate cause from coincidence is a superpower. By embracing causal inference, the next generation of data scientists can break free of the limits of correlation and chart the course toward a more impactful, explanatory science of data. The journey has only begun, and the possibilities are endless.
