Measurement bias and effect restoration in causal inference

This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, 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.