On Measurement Bias in Causal Inference
This addresses bias issues in causal inference for researchers and practitioners, but it appears incremental as it builds on existing methods.
The paper tackles measurement errors in causal inference and presents algebraic and graphical methods to eliminate systematic bias, focusing on controlling partially observable confounders in parametric and non-parametric models.
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.