The Seven Tools of Causal Inference with Reflections on Machine Learning

JUDEA PEARL, UCLA Computer Science Department, USA
ACM Reference Format:
Judea Pearl. 2018. The Seven Tools of Causal Inference with Reflections on
Machine Learning. 1, 1 (November 2018), 6 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

The dramatic success in machine learning has led to an explosion of AI applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations, however, have met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability or robustness. Machine learning researchers have noted that current systems lack the capability of recognizing or reacting to new circumstances they have not been specifically programmed or trained for. Intensive theoretical and experimental efforts toward “transfer learning,” “domain adaptation,” and “Lifelong learning” [Chen and Liu 2016] are reflective of this obstacle

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