Consider an organization seeking to improve their operations, using their historical data. During this type of analysis the commonly known fact that “correlation does not imply causation” comes to life. It is crucial to distinguish between events that *cause* existing inefficiencies and those that merely correlate. Spending money to fix something that is not the root cause of the problem could be an expensive folly. Causal inference aims to determine which available controls drive specific outcomes. This is a distinctly more demanding condition than learning the correlation. Many machine learning approaches disregard causal inference, despite a wide range of approaches to causal inference having been proposed in the literature. This talk will discuss the importance of causal models, as well as some of the most state-of-the-art methods for reasoning.
Paul Beaumont is a Senior Data Scientist at QuantumBlack, an advanced analytics consultancy based in Singapore. He works on statistical models for explanatory, predictive and prescriptive problems, and his role involves designing mathematical models to help clients understand pertinent questions about their data. Paul holds a PhD in Mathematics & Computer Science from Imperial College London, and leads QuantumBlack’s R&D efforts in Causal Inference.
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