We write about cause and effect a lot in the field of education and learning technology. After all, we're mostly writing about tools and outcomes, and tools that don't cause outcomes aren't especially useful. But what is causation? This article is a refreshing and detailed look at what we mean by causation in the 21st century, based on a progression from association through experimentation and finally to counterfactual reasoning, a journal that takes us through things like P-values, confounders, colliders, and causal graphs. This article is accessible and yet doesn't really skimp on the important detail researchers and writers need. "As 'data science' became a buzzword, we got lazy: we thought that, if we could just gather enough data, correlation would be good enough..., and correlation still hasn't gotten us what we want: the ability to understand cause and effect. But as we've seen, it is possible to go further... causal graphs provide new tools and techniques for thinking about the relationships between possible causes."
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