  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.
