The Causal Inference Framework: A Primer on Concepts and Methods for Improving the Study of Well-Woman Childbearing Processes

Research output: Contribution to journalReview article

4 Citations (Scopus)

Abstract

The causal inference framework and related methods have emerged as vital within epidemiology. Scientists in many fields have found that this framework and a variety of designs and analytic approaches facilitate the conduct of strong science. These approaches have proven particularly important for catalyzing knowledge development using existing data and addressing questions for which randomized clinical trials are neither feasible nor ethical. The study of healthy women and normal childbearing processes may benefit from more direct and deliberate engagement with the process of inferring causes and, further, may be strengthened through use of methods appropriate for this undertaking. The purpose of this primer, the first in a series of 3 articles, is to provide the reader an introduction to concepts and methods relevant for causal inference, aimed at the clinician scientist and offer details and references supporting further application of epidemiologic knowledge. The causal inference framework and associated methods hold promise for generating strong, broadly representative, and actionable science to improve the outcomes of healthy women during the childbearing cycle and their children.

Original languageEnglish (US)
Pages (from-to)700-709
Number of pages10
JournalJournal of Midwifery and Women's Health
Volume63
Issue number6
DOIs
StatePublished - Nov 1 2018

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Epidemiology
Randomized Controlled Trials

Keywords

  • causal inference framework
  • directed acyclic graphs
  • midwifery science
  • observational studies
  • physiologic childbearing science
  • primer
  • propensity score analysis
  • secondary data analysis

ASJC Scopus subject areas

  • Obstetrics and Gynecology
  • Maternity and Midwifery

Cite this

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abstract = "The causal inference framework and related methods have emerged as vital within epidemiology. Scientists in many fields have found that this framework and a variety of designs and analytic approaches facilitate the conduct of strong science. These approaches have proven particularly important for catalyzing knowledge development using existing data and addressing questions for which randomized clinical trials are neither feasible nor ethical. The study of healthy women and normal childbearing processes may benefit from more direct and deliberate engagement with the process of inferring causes and, further, may be strengthened through use of methods appropriate for this undertaking. The purpose of this primer, the first in a series of 3 articles, is to provide the reader an introduction to concepts and methods relevant for causal inference, aimed at the clinician scientist and offer details and references supporting further application of epidemiologic knowledge. The causal inference framework and associated methods hold promise for generating strong, broadly representative, and actionable science to improve the outcomes of healthy women during the childbearing cycle and their children.",
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