title: Imputation approaches for potential outcomes in causal inference.
abstract: BACKGROUND
  The fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature, the connections between causal inference and missing data can provide additional intuition.
  
  
  METHODS
  We demonstrate how we can approach causal inference in ways similar to how we address all problems of missing data, using multiple imputation and the parametric g-formula.
  
  
  RESULTS
  We explain and demonstrate the use of these methods in example data, and discuss implications for more traditional approaches to causal inference.
  
  
  CONCLUSIONS
  Though there are advantages and disadvantages to both multiple imputation and g-formula approaches, epidemiologists can benefit from thinking about their causal inference problems as problems of missing data, as such perspectives may lend new and clarifying insights to their analyses.
authors.affiliation: Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, NC, USA, djw@unc.edu.
authors.forename: Daniel
authors.initials: D
authors.lastname: Westreich
authors.affiliation: Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, NC, USA.
authors.forename: Jessie K
authors.initials: JK
authors.lastname: Edwards
authors.affiliation: Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, NC, USA.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, QC, Canada and.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Platt
authors.affiliation: Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA.
authors.forename: Sunni L
authors.initials: SL
authors.lastname: Mumford
authors.affiliation: Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA.
authors.forename: Enrique F
authors.initials: EF
authors.lastname: Schisterman
mesh_terms: D015984:Causality; D004813:Epidemiology / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D012107:Research Design / Q000592:standards*
keywords: Causal inference; g-formula; multiple imputation; potential outcomes