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Seven basic rules for causal inference

Personal website of Dr. Peder M. Isager

In this blog post I will describe seven basic rules that govern the relationship between causal mechanisms in the real world and associations/correlations we can observe in data. Exchangeability: “the conditional probability of receiving every value of treatment, though not decided by the investigators, depends only on measured covariates” Hernán and Robins ( 2020). Posted on: August 13, 2024 Length: 10 minute read, 2034 words Tags: causal inferencecausal graphscorrelation See Also: Why does correlation not equal causation?Fifty ways to leave your model

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