On Measurement Bias in Causal Inference

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.

arXiv commentAppears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

References in corpus (1)

• Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources

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• Generating Synthetic Text Data to Evaluate Causal Inference Methods
• Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysis
• Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models