Causal Inference: Overview

Jennifer Hill, Elizabeth Stuart

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This article discusses causal inference in statistics. It describes the theoretical framework and notation needed to formally define causal effects and the assumptions required to identify them nonparametrically. This involves definition of potential outcomes that represent the potential value of the outcome across different treatment exposures. Designs that allow researchers to satisfy or weaken these assumptions are briefly described. Then common parametric assumptions used to model effects and more current approaches that require weaker assumptions are discussed.

Original languageEnglish (US)
Title of host publicationInternational Encyclopedia of the Social & Behavioral Sciences: Second Edition
PublisherElsevier Inc.
Pages255-260
Number of pages6
ISBN (Electronic)9780080970875
ISBN (Print)9780080970868
DOIs
StatePublished - Mar 26 2015

Keywords

  • Causal inference
  • Common support
  • Ignorability
  • Observational studies
  • Overlap
  • Potential outcomes
  • Propensity scores
  • Quasi-experiments
  • Randomized experiments
  • Regression
  • SUTVA

ASJC Scopus subject areas

  • Social Sciences(all)

Fingerprint Dive into the research topics of 'Causal Inference: Overview'. Together they form a unique fingerprint.

Cite this