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An Introduction to Causal Inference by Judea Pearl

By Judea Pearl

This summarizes fresh advances in causal inference and underscores the paradigmatic shifts that has to be undertaken in relocating from conventional statistical research to causal research of multivariate facts. distinct emphasis is put on the assumptions that underlie all causal inferences, the languages utilized in formulating these assumptions, the conditional nature of all causal and counterfactual claims, and the equipment which were constructed for the overview of such claims. those advances are illustrated utilizing a basic conception of causation according to the Structural Causal version (SCM), which subsumes and unifies different ways to causation, and gives a coherent mathematical starting place for the research of factors and counterfactuals. particularly, the paper surveys the improvement of mathematical instruments for inferring (from a mix of knowledge and assumptions) solutions to 3 forms of causal queries: these approximately (1) the results of capability interventions, (2) chances of counterfactuals, and (3) direct and oblique results (also referred to as "mediation"). ultimately, the paper defines the formal and conceptual relationships among the structural and potential-outcome frameworks and provides instruments for a symbiotic research that makes use of the powerful positive aspects of either. The instruments are established within the analyses of mediation, explanations of results, and possibilities of causation.

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25) directly, by selecting a sufficient set S directly from the diagram, without manipulating the truncated factorization formula. The selection criterion can be applied systematically to diagrams of any size and shape, thus freeing analysts from judging whether “X is conditionally ignorable given S,” a formidable mental task required in the potential-response framework (Rosenbaum and Rubin, 1983). The criterion also enables the analyst to search for an optimal set of covariate—namely, a set S that minimizes measurement cost or sampling variability (Tian, Paz, and Pearl, 1998).

We do this by deriving and comparing the expressions for these two quantities, as defined by (5) and (6), respectively. The mutilated model in Eq. (6) dictates: (11) E(Y|do(x0)) = E(fY(x0, uY)), whereas the pre-intervention model of Eq. (5) gives (12) which is identical to (11). Therefore, (13) E(Y|do(x0)) = E(Y|X = x0)) Using a similar derivation, though somewhat more involved, we can show that P(y|do(x)) is identifiable and given by the conditional probability P(y|x). We see that the derivation of (13) was enabled by two assumptions; first, Y is a function of X and UY only, and, second, UY is independent of {UZ, UX}, hence of X.

In Fig. 1, for example, the absence of arrow from Y to X represents the claim that symptom Y is not among the factors UX which affect disease X. Thus, in our example, the complete model of a symptom and a disease would be written as in Fig. 1: The diagram encodes the possible existence of (direct) causal influence of X on Y, and the absence of causal influence of Y on X, while the equations encode the quantitative relationships among the variables involved, to be determined from the data. The parameter β in the equation is called a “path coefficient” and it quantifies the (direct) causal effect of X on Y; given the numerical values of β and UY, the equation claims that, a unit increase for X would result in β units increase of Y regardless of the values taken by other variables in the model, and regardless of whether the increase in X originates from external or internal influences.

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