title: Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.
abstract: This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.
authors: Quantitative Analyst, Google Inc., Mountain View, California 94043, USA.
authors: Amy
authors: A
authors: Richardson
authors: Associate Professor, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
authors: Michael G
authors: MG
authors: Hudgens
authors: Member, Statistical Center for HIV/AIDS Research and Prevention (SCHARP), Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA.
authors: Peter B
authors: PB
authors: Gilbert
authors: Professor, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
authors: Jason P
authors: JP
authors: Fine
keywords: Causal inference; nonparametric bounds; partially identifiable models; sensitivity analysis