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On Measurement Bias in Causal Inference (1203.3504v1)
Published 15 Mar 2012 in stat.ME and cs.AI
Abstract: This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias-free effect estimates in such models.