The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods

Sekhon, Jasjeet S.

doi:10.1093/oxfordhb/9780199286546.003.0011

Cited by 92 publications

(75 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Observational studies are often confounded by the impossibility of simultaneously observing treatment and non-treatment effects in the same individual or groups of individuals. This has been referred to as the fundamental problem of causal inference (Rubin 1974;see also Holland 1986;Sekhon 2007). By randomly assigning a group of subjects into two groups: a treatment condition in which respondents are subjected to a causal variable and a control group in which they are not, experimenters can come as close as possible to overcoming the fundamental problem of causal inference and can assume that any difference in the two groups on an outcome variable of interest must be attributable to the treatment.…”

Section: Experiments In Political Sciencementioning

confidence: 99%

Judicialization and Its Effects: Experiments as a Way Forward

Hevron

2018

Laws

View full text Add to dashboard Cite

Law and courts play a larger role in American policymaking than in similar countries-and a larger role than ever before in American politics. However, systematic efforts to evaluate the effects of judicialized policymaking are consistently plagued by problems of causal inference. Experiments offer a way forward. Causal claims by public law scholars are often undercut by validity difficulties that are avoidable if scholars engaging in observational research incorporate the tenets of experiments in their research designs, as well as if more public law scholars attempted to isolate the effects of judicialization in controlled settings, such as survey or laboratory experiments. An original survey experiment on the effects of media reporting on tort reform suggests that experiments have much to offer public law scholars. Despite certain challenges in implementation, experiments and observational research based on experiments provide a promising path for assessing the varied-and important-effects of judicialized policymaking.

show abstract

Section: Experiments In Political Sciencementioning

confidence: 99%

Judicialization and Its Effects: Experiments as a Way Forward

Hevron

2018

Laws

View full text Add to dashboard Cite

show abstract

“…The potential (or counterfactual) outcomes framework is becoming increasingly popular in a wide array of empirical sciences, including epidemiology [15,16]. The foremost strength of this framework is the clarity it offers in defining causal effects, particularly in the context of observational studies [17].…”

Section: The Counterfactual Frameworkmentioning

confidence: 99%

The Counterfactual Implications of Fundamental Cause Theory

Naimi

2016

Curr Epidemiol Rep

View full text Add to dashboard Cite

Causal inference lies at the center of epidemiologic research. In social epidemiology, two separate approaches to framing cause-effect relations have been considered: the counterfactual (or potential outcomes) framework and the theory of fundamental causes. The relations between these two frameworks have not yet been articulated. In this paper, I review the counterfactual and fundamental cause frameworks, and show how they capture different notions of cause-effect relations. Additionally, I show how the counterfactual and fundamental cause frameworks can be integrated to provide a more rigorous treatment of causality in social epidemiology. In particular, I show how counterfactual quantities can be used to evaluate predictions that follow from fundamental cause theory, assess the relations between and roles of various social resources in a given health disparity, and generate evidence on the potential interventions to mitigate health disparities.

show abstract

“…The potential outcome framework is also known as the Neyman-Rubin causal model [30], and it was Rubin who first recognized that use of this notation implicitly makes a "stable unit treatment value assumption" (SUTVA) or the assumption that [31]: (i) there is only one "version" of the exposure; and (ii) no subject's exposure can affect another subject's potential outcome. Regarding assumption (i), "version" is meant to connote, for example, different ways in which a subject might get exposed to a particular level of X, which may result in different causal effects.…”

Section: Sutva: Counterfactual Consistencymentioning

confidence: 99%

Counterfactual Theory in Social Epidemiology: Reconciling Analysis and Action for the Social Determinants of Health

Naimi

Kaufman

2015

Curr Epidemiol Rep

View full text Add to dashboard Cite

There is a strong and growing interest in applying formal methods for causal inference with observational data in social epidemiology. A number of challenges in defining, identifying, and estimating counterfactual-based causal effects have been especially problematic in social epidemiology, particularly for commonly used exposures such as race, education, occupation, or socioeconomic position. The purpose of this article is to revisit these challenges in light of the conceptual and analytic advancements in causal inference over the last two decades. We focus on a central assumption for causal inference known as the stable unit treatment value assumption, which can be divided into two component assumptions: counterfactual consistency and the absence of interference. We give simple hypothetical examples to illustrate how and why these assumptions are often violated in research on the social determinants of health (e.g., education, race/ethnicity, socioeconomic position) and provide strategies that can be used to sidestep these assumptions. In particular, we note that a recently proposed mediation analysis strategy can be used to explore questions about health disparities in a more formal causal inference framework. We emphasize that a central obstacle to estimating causal effects variables such as race, education (e.g., high school versus no high school), or occupation is the need to identify an intervention (possibly hypothetical) that will lead to changes in the exposure of interest.

show abstract

The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods

Cited by 92 publications

References 65 publications

Judicialization and Its Effects: Experiments as a Way Forward

Judicialization and Its Effects: Experiments as a Way Forward

The Counterfactual Implications of Fundamental Cause Theory

Counterfactual Theory in Social Epidemiology: Reconciling Analysis and Action for the Social Determinants of Health

Contact Info

Product

Resources

About