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  "body": "\n## Definition\n\n*Causal inference* is the branch of [[statistical inference|JZ2S01BHJJ]] whose targets are causal quantities.\nThese are the values an outcome would take under interventions that set a treatment or exposure to given values.\nIn the [[potential outcomes framework|23ATBZMJHR]], a typical target is the average treatment effect $E[Y(1) - Y(0)]$.\nHere $Y(w)$ is the outcome a unit would have under treatment level $w$.\nA causal quantity is *identified* if, under stated assumptions, it is a function of the distribution of the observed variables.\n\n## Remarks\n\nCausal inference has two steps: identification, which shows that the target is a function of the observed distribution under assumptions, and estimation of that function from data.\n\nExample: in a completely randomised experiment with treatment $W \\in \\set{0, 1}$, randomization makes $W$ independent of $(Y(0), Y(1))$.\nThen $E[Y(1) - Y(0)] = E[Y \\mid W = 1] - E[Y \\mid W = 0]$, so the average treatment effect is identified and estimated by the difference of group means.\n\nNon-example: estimating $E[Y \\mid W = 1] - E[Y \\mid W = 0]$ in observational data, with no assumption linking it to $Y(1)$ and $Y(0)$, is a description of an association, not causal inference.\nIf sicker patients choose the treatment, this difference may even have the opposite sign to the causal effect.\n",
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