Episode 12: Your Machine Learning Solves The Wrong Problem
Mar 13, 2025
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Stefan Wager, a Stanford professor and expert in causal machine learning, dives into the misalignments between prediction and decision-making. He argues that traditional machine learning often neglects the crucial 'what-if' questions businesses face. Stefan shares insights on causal relationships and emphasizes the need for robust experimentation to make informed decisions. He explores the role of causal ML in enhancing customer engagement and optimizing revenue, while also discussing common pitfalls in experimental design, making a compelling case for collaborative learning in the data science field.
Traditional machine learning prioritizes prediction over innovation, often leading to inadequate outcomes when businesses seek to drive change.
Causal machine learning empowers organizations to address critical 'what-if' scenarios, allowing for more informed strategic decisions based on action-outcome relationships.
Implementing causal ML requires careful preparation, including designing experiments that provide reliable data to enhance understanding of causal relationships.
Deep dives
Limitations of Classical Machine Learning
Classical machine learning primarily focuses on predicting outcomes based on historical data, which often results in merely describing the status quo rather than innovating or creating new opportunities. This approach is insufficient for entities aiming to change the market landscape or develop groundbreaking solutions, as exemplified by the hypothetical case of Airbnb in 2008, where prediction algorithms would yield no viable outcomes due to the company's non-existence. Thus, relying solely on prediction can limit progress when innovation and proactive decision-making are critical. Transitioning towards causal ML offers a framework to directly link actions with outcomes, addressing more impactful questions beyond mere prediction.
The Importance of Causal Inference
Causal inference enhances decision-making by enabling organizations to ask 'what if' questions, allowing them to quantify how changes in policies or strategies can affect different outcomes. By implementing causal ML, businesses can explore alternative scenarios that inform strategy, such as optimizing pricing or addressing customer churn more effectively. The distinction lies in causal ML's ability to provide actionable insights by demonstrating potential outcomes resulting from specific decisions, rather than simply predicting what will happen based on past data. This shift transforms the nature of analysis, focusing on the interdependencies between actions and results.
Integrating Causal ML into Workflows
Introducing causal ML necessitates a clear understanding of the action space and desired outcomes to align the methodology with business objectives effectively. Organizations must first recognize the connection between their chosen actions and anticipated results to apply causal ML appropriately, thus avoiding common pitfalls associated with basic prediction models. Building a robust causal ML framework requires extensive preparatory steps, including collecting the right experimental data and defining the questions to be answered. Software tools, like the GRF package, facilitate this integration, allowing teams to leverage the strengths of causal ML for more nuanced analyses.
Churn Prevention and Response Targeting
Customer churn prediction exemplifies the limitations of traditional machine learning, where merely identifying likely churners does not provide actionable insights for retention strategies. An illustrative example reveals that targeting loyalty gifts at customers predicted to churn was less effective than offering them to loyal patrons who had not engaged recently, demonstrating that understanding the causal relationship of customer behavior is crucial. This misalignment highlights how traditional predictive models can lead to misguided interventions that undercut business objectives. By employing causal ML, organizations can better assess treatment effects and refine their engagement tactics based on actual customer responsiveness.
The Need for Experimental Data in Causal Analysis
To unlock the potential of causal ML, organizations must prioritize the implementation of experiments that yield reliable data essential for causal analysis. While observational data has its uses, the connection between actions and outcomes can become confounded without the rigor of randomized trials, which eliminate biases inherent in traditional datasets. Conversely, executing experiments can be resource-intensive and may raise logistical challenges; however, the benefits of running well-structured experiments far outweigh the costs when organizations seek reliable insights for decision-making. This foundational requirement underscores the fundamental shift from predictive analytics to a more comprehensive approach that incorporates causation through robust experimentation.
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
