Measurement Bias and Effect Restoration 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 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.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2011
Accession Number
ADA557455

Entities

People

  • Judea Pearl
  • Manabu Kuroki

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Networks
  • Coefficients
  • Data Science
  • Eigenvalues
  • Equations
  • Factor Analysis
  • Health Care
  • Information Science
  • Measurement
  • Notation
  • Pilot Studies
  • Probability
  • Probability Distributions
  • Reasoning
  • Regression Analysis
  • Statistics

Readers

  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

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