On Measurement Bias in Causal Inference
Methodology
2012-03-19 v1 Artificial Intelligence
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.
Cite
@article{arxiv.1203.3504,
title = {On Measurement Bias in Causal Inference},
author = {Judea Pearl},
journal= {arXiv preprint arXiv:1203.3504},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)