scholarly journals Applying a potential outcomes framework to estimate policy-relevant effects of exposure mixtures

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jessie P. Buckley
Keyword(s):  
Potential Outcomes ◽  
Potential Outcomes Framework

Causal mediation in developmental science: A primer

2021 ◽  
pp. 016502542098164
Author(s):  
Jorge Cuartas ◽  
Dana Charles McCoy
Keyword(s):  
Best Practices ◽  
Mediation Analysis ◽  
Critical Role ◽  
Experimental Studies ◽  
Developmental Theory ◽  
Potential Outcomes ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Developmental Science ◽  
Potential Outcomes Framework

Mediation has played a critical role in developmental theory and research. Yet, developmentalists rarely discuss the methodological challenges of establishing causality in mediation analysis or potential strategies to improve the identification of causal mediation effects. In this article, we discuss the potential outcomes framework from statistics as a means for highlighting several fundamental challenges of establishing causality in mediation analysis, including the difficulty of meeting the key assumption of sequential ignorability, even in experimental studies. We argue that this framework—which, although commonplace in other fields, has not yet been taken up in developmental science—can inform solutions to these challenges. Based on the framework, we offer a series of recommendations for improving causal inference in mediation analysis, including an overview of best practices in both study design and analysis, as well as resources for conducting analysis. In doing so, our overall objective in this article is to support the use of rigorous methods for understanding questions of mechanism in developmental science.

scholarly journals Invited Commentary: Counterfactuals in Social Epidemiology—Thinking Outside of “the Box”

2019 ◽  
Vol 189 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Tyler J VanderWeele
Keyword(s):  
Social Movements ◽  
Causal Inference ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Social Epidemiology ◽  
Determinants Of Health ◽  
Potential Outcomes ◽  
The Social ◽  
Potential Outcomes Framework

Abstract There are tensions inherent between many of the social exposures examined within social epidemiology and the assumptions embedded in quantitative potential-outcomes-based causal inference framework. The potential-outcomes framework characteristically requires a well-defined hypothetical intervention. As noted by Galea and Hernán (Am J Epidemiol. 2020;189(3):167–170), for many social exposures, such well-defined hypothetical exposures do not exist or there is no consensus on what they might be. Nevertheless, the quantitative potential-outcomes framework can still be useful for the study of some of these social exposures by creative adaptations that 1) redefine the exposure, 2) separate the exposure from the hypothetical intervention, or 3) allow for a distribution of hypothetical interventions. These various approaches and adaptations are reviewed and discussed. However, even these approaches have their limits. For certain important historical and social determinants of health such as social movements or wars, the quantitative potential-outcomes framework with well-defined hypothetical interventions is the wrong tool. Other modes of inquiry are needed.

scholarly journals Causal inference under multiple versions of treatment

2013 ◽  
Vol 1 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Tyler J. VanderWeele ◽  
Miguel A. Hernan
Keyword(s):  
Causal Inference ◽  
Treatment Effect ◽  
Indirect Effects ◽  
Causal Effect ◽  
Potential Outcomes ◽  
Direct And Indirect Effects ◽  
Treatment Variable ◽  
Effect Of Treatment ◽  
New Treatment ◽  
Potential Outcomes Framework

Abstract: In this article, we discuss causal inference when there are multiple versions of treatment. The potential outcomes framework, as articulated by Rubin, makes an assumption of no multiple versions of treatment, and here we discuss an extension of this potential outcomes framework to accommodate causal inference under violations of this assumption. A variety of examples are discussed in which the assumption may be violated. Identification results are provided for the overall treatment effect and the effect of treatment on the treated when multiple versions of treatment are present and also for the causal effect comparing a version of one treatment to some other version of the same or a different treatment. Further identification and interpretative results are given for cases in which the version precedes the treatment as when an underlying treatment variable is coarsened or dichotomized to create a new treatment variable for which there are effectively “multiple versions”. Results are also given for effects defined by setting the version of treatment to a prespecified distribution. Some of the identification results bear resemblance to identification results in the literature on direct and indirect effects. We describe some settings in which ignoring multiple versions of treatment, even when present, will not lead to incorrect inferences.

scholarly journals Assessing Treatment-Selection Markers using a Potential Outcomes Framework

Biometrics ◽  
2012 ◽  
Vol 68 (3) ◽  
pp. 687-696 ◽  
Author(s):  
Ying Huang ◽  
Peter B. Gilbert ◽  
Holly Janes
Keyword(s):  
Treatment Selection ◽  
Potential Outcomes ◽  
Potential Outcomes Framework ◽  
Selection Markers
Sign in / Sign up

Export Citation Format

Share Document