Uncertainty in Artificial Intelligence
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On Measurement Bias in Causal Inference
Judea Pearl
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.
Pages: 425-432
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PDF Link: /papers/10/p425-pearl.pdf
AUTHOR = "Judea Pearl ",
TITLE = "On Measurement Bias in Causal Inference",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "425--432"

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