Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A graphical criterion for the identification of causal effects in linear models
Eighteenth national conference on Artificial intelligence
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The Identification problem concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. Traditional approaches to this problem are based on algebraic manipulations of the equations defining the model. In this paper, we propose a new approach to the problem which takes advantage of the graphical representation of the model.