Accession Number:

ADA522064

Title:

On Measurement Bias in Causal Inference

Descriptive Note:

Technical rept.

Corporate Author:

CALIFORNIA UNIV LOS ANGELES DEPT OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2010-01-25

Pagination or Media Count:

13.0

Abstract:

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

Subject Categories:

  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE