Transportability Across Studies: A Formal Approach

Abstract

We provide a formal definition of the notion of "transportability," or "external validity," which we view as a license to transfer causal information learned in experimental studies to a different environment, in which only observational studies can be conducted. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target environment can be inferred from experimental findings in a different environment. When the answer is affirmative, the procedures identify the set of experimental and observational studies that need be conducted to license the transport. We further demonstrate how transportability analysis can guide the transfer of knowledge among non-experimental studies to minimize re-measurement cost and improve prediction power. We further provide a causally principled definition of "surrogate endpoint" and show that the theory of transportability can assist the identification of valid surrogates in a complex network of cause-effect relationships.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2011
Accession Number
ADA557437

Entities

People

  • Elias Bareinboim
  • Judea Pearl

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Age Distribution
  • Artificial Intelligence
  • Clinical Trials
  • Computer Science
  • Data Analysis
  • Demography
  • Disparities
  • Environment
  • Formal Languages
  • Identification
  • Language
  • Mathematical Analysis
  • Measurement
  • Probability
  • Probability Distributions
  • Statistics
  • Transport Ships

Fields of Study

  • Computer science

Readers

  • Logistics and Supply Chain Management.
  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks