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Solving for Why

By Marina Krakovsky

Communications of the ACM, Vol. 65 No. 2, Pages 11-13

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Thanks to large datasets and machine learning, computers have become surprisingly adept at finding statistical relationships among many variables—and exploiting these patterns to make useful predictions. Whether the task involves recognizing objects in photographs or translating text from one language to another, much of what today's intelligent machines can accomplish stems from the computers' ability to make predictions based on statistical associations, or correlations.

By and large, computers are very good at this kind of prediction. Yet for many tasks, that is not enough. "In reality, we often want to not only predict things, but we want to improve things," says Jonas Peters, a professor of statistics at the University of Copenhagen. "This is what you need causal methods for," explains Peters, co-author of a book about causal inference, a field of study that in recent years has gained interest in computing and other sciences. As the field has developed, it has built up more and more mathematical rigor, giving scientists across a variety of disciplines a formal language for explicitly expressing their assumptions and better tools for acquiring new knowledge.


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