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  "body": "\n## Summary\n\nCausal inference is the part of statistics that asks what would happen to an outcome if one intervened on a variable.\nThis differs from asking which values tend to occur together.\nIt makes precise the difference between correlation and causation, through frameworks such as the [[Rubin causal model|23ATBZMJHR]] of potential outcomes and causal graphs.\nIn a randomised experiment causal effects can be estimated by simple comparisons, thanks to [[randomization|734Q6Q51X7]].\nWith observational data one needs extra assumptions, such as the absence of unmeasured confounders, or special designs such as [[instrumental variables|6ETK64HYK6]].\n\n## Explanations\n\n### L1 human\n\nPeople who carry umbrellas get wet more often than people who do not, but umbrellas do not cause rain.\nCausal inference studies when data can tell us what a change would actually do, for example whether a drug lowers blood pressure.\nThe key problem is that each person either takes the drug or does not, so we never see both outcomes for the same person.\nA coin flip that decides who gets the drug makes the two groups comparable, so the difference of their averages measures the effect.\nWithout such a coin flip, hidden differences between the groups can create or hide an effect, as in [[Simpson's paradox|T060JHSH05]].\n",
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