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AIIM 1995

Causal inference from indirect experiments

Journal Article journal-article Artificial Intelligence ยท Artificial Intelligence in Medicine

Abstract

An indirect experiment is a study in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced, to receive a given treatment program. The purpose of this paper is to bring to the attention of experimental researchers simple mathematical results that enable us to assess, from indirect experiments, the strength with which causal influences operate among variables of interest. The results reveal that despite the laxity of the encouraging instrument, data from indirect experimentation can yield significant and sometimes accurate information on the impact of a program on the population as a whole, as well as on the particular individuals who participated in the program.

Authors

Keywords

  • Causal reasoning
  • Treatment evaluation
  • Noncompliance
  • Graphical models

Context

Venue
Artificial Intelligence in Medicine
Archive span
1989-2026
Indexed papers
2812
Paper id
985624372078878894
v2026.09.13