Uncertainty in Artificial Intelligence
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On Measurement Bias in Causal Inference
Judea Pearl
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.
Keywords:
Pages: 425-432
PS Link:
PDF Link: /papers/10/p425-pearl.pdf
BibTex:
@INPROCEEDINGS{Pearl10,
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)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "425--432"
}


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