Generalizing Experimental Findings

Abstract

This note examines one of the most crucial questions in causal inference: "How generalizable are randomized clinical trials?" The question has received a formal treatment recently, using a non-parametric setting which has led to a simple and general solution. I will describe this solution and several of its ramifications, and compare it to the way researchers have attempted to tackle the problem using the language of ignorability. We will see that ignorability-type assumptions need to be enriched with structural assumptions in order to capture the full spectrum of conditions that permit generalizations, and in order to judge their plausibility in specific applications.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2015
Accession Number
ADA621962

Entities

People

  • Judea Pearl

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Calibration
  • Computer Science
  • Disparities
  • Education
  • Experimental Data
  • Information Operations
  • Judgment
  • Probability
  • Probability Distributions
  • Standards
  • Stratification
  • Training

Readers

  • Artificial Intelligence
  • Theoretical Analysis.

Technology Areas

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