On Measurement Bias in Causal Inference

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 25, 2010
Accession Number
ADA522064

Entities

People

  • Judea Pearl

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Coefficients
  • Computer Science
  • Epidemiology
  • Equations
  • Errors
  • Greenland
  • Identification
  • Intelligent Systems
  • Linear Systems
  • Literature
  • Measurement
  • Pilot Studies
  • Probability
  • Reasoning
  • Regression Analysis
  • Statistics

Readers

  • Artificial Intelligence
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms