Literature DB >> 18050151

Ignorability and stability assumptions in neighborhood effects research.

Tyler J Vanderweele1.   

Abstract

Two central assumptions concerning causal inference in the potential outcomes framework for estimating neighborhood effects are examined. The stable unit treatment value assumption in the context of neighborhood effects requires that an individual's outcome does not depend on the treatment assigned to neighborhoods other than the individual's own neighborhood. The assumption is important in that it makes estimation feasible, although some progress can be made even when the assumption is relaxed. Some discussion is given concerning the contexts in which the neighborhood-level stable unit treatment value assumption is likely to hold. The ignorability assumption allows the researcher to move from conclusions about association to conclusions about causation. In the context of neighborhood-wide interventions, the ignorability assumption for the individual-level potential outcomes framework can be easily adapted for neighborhood effects. Copyright (c) 2007 John Wiley & Sons, Ltd.

Mesh:

Year:  2008        PMID: 18050151     DOI: 10.1002/sim.3139

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  19 in total

1.  Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods.

Authors:  Jennifer Ahern; Alan Hubbard; Sandro Galea
Journal:  Am J Epidemiol       Date:  2009-03-06       Impact factor: 4.897

2.  Predicting the Population Health Impacts of Community Interventions: The Case of Alcohol Outlets and Binge Drinking.

Authors:  Jennifer Ahern; K Ellicott Colson; Claire Margerson-Zilko; Alan Hubbard; Sandro Galea
Journal:  Am J Public Health       Date:  2016-09-15       Impact factor: 9.308

3.  Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis.

Authors:  David P MacKinnon; Angela G Pirlott
Journal:  Pers Soc Psychol Rev       Date:  2014-07-25

4.  Evaluating the impact of implementation factors on family-based prevention programming: methods for strengthening causal inference.

Authors:  D Max Crowley; Donna L Coffman; Mark E Feinberg; Mark T Greenberg; Richard L Spoth
Journal:  Prev Sci       Date:  2014-04

5.  Direct and indirect effects for neighborhood-based clustered and longitudinal data.

Authors:  T J VanderWeele
Journal:  Sociol Methods Res       Date:  2010-05-01

6.  Effectiveness of Potential Interventions to Change Gendered Social Norms on Prevalence of Intimate Partner Violence in Uganda: a Causal Inference Approach.

Authors:  Damazo T Kadengye; Samuel Iddi; Lauren Hunter; Sandra I McCoy
Journal:  Prev Sci       Date:  2019-10

7.  The roles of outlet density and norms in alcohol use disorder.

Authors:  Jennifer Ahern; Laura Balzer; Sandro Galea
Journal:  Drug Alcohol Depend       Date:  2015-03-24       Impact factor: 4.492

8.  Assessing mediation using marginal structural models in the presence of confounding and moderation.

Authors:  Donna L Coffman; Wei Zhong
Journal:  Psychol Methods       Date:  2012-08-20

9.  Propensity score weighting with multilevel data.

Authors:  Fan Li; Alan M Zaslavsky; Mary Beth Landrum
Journal:  Stat Med       Date:  2013-03-24       Impact factor: 2.373

10.  Navigating non-positivity in neighbourhood studies: an analysis of collective efficacy and violence.

Authors:  Jennifer Ahern; Magdalena Cerdá; Sheri A Lippman; Kenneth J Tardiff; David Vlahov; Sandro Galea
Journal:  J Epidemiol Community Health       Date:  2012-08-22       Impact factor: 3.710

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.