An Introduction to Causal Inference by Judea Pearl

By Judea Pearl

This booklet 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 info. specific 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 evaluation of such claims. those advances are illustrated utilizing a basic idea of causation in line with the Structural Causal version (SCM), which subsumes and unifies different ways to causation, and offers a coherent mathematical origin for the research of reasons and counterfactuals. specifically, the paper surveys the improvement of mathematical instruments for inferring (from a mix of information and assumptions) solutions to 3 varieties of causal queries: these approximately (1) the results of power interventions, (2) chances of counterfactuals, and (3) direct and oblique results (also referred to as "mediation"). eventually, 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 factors of either. The instruments are validated within the analyses of mediation, explanations of results, and possibilities of causation.

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Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. 1. Introduction Most studies in the health, social and behavioral sciences aim to answer causal rather than associative – questions. Such questions require some knowledge of the data-generating process, and cannot be computed from the data alone, nor from the distributions that govern the data.

Similarly, the non-admissible sets T = {Z2} and Z = {W2, Z2} are c-equivalent, since their Markov boundaries are the same (Tm = Zm = {Z2}). In contrast, the sets {W1} and {Z1}, although they block the same set of paths in the graph, are not c-equivalent; they fail both conditions of Theorem 2. Tests for c-equivalence (27) are fairly easy to perform, and they can also be assisted by propensity scores methods. The information that such tests provide can be as powerful as conditional independence tests.

Time varying treatments), conditional policies, and surrogate experiments were developed in Pearl and Robins (1995), Kuroki and Miyakawa (1999), and Pearl (2000a, Chapters 3–4). A more recent analysis (Tian and Pearl, 2002) shows that the key to identifiability lies not in blocking paths between X and Y but, rather, in blocking paths between X and its immediate successors on the pathways to Y. 7 For example, if W3 is the only observed covariate in the model of Fig. 4, P(y|do(x)) can be estimated since every path from X to W3 (the only child of X) traces either the arrow X → W3, or the arrow W3 → Y, both emanating from a measured variable (W3).

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