Journal ArticleDOI
What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference
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TLDR
A frame-work for causal inference when interference is present is developed and a number of causal estimands of interest are defined, which are of great importance for a researcher who fails to recognize that a treatment is beneficial when in fact it is universally harmful.Abstract:
During the past 20 years, social scientists using observational studies have generated a large and inconclusive literature on neighborhood effects. Recent workers have argued that estimates of neighborhood effects based on randomized studies of housing mobility, such as the “Moving to Opportunity” (MTO) demonstration, are more credible. These estimates are based on the implicit assumption of no interference between units; that is, a subject's value on the response depends only on the treatment to which that subject is assigned, not on the treatment assignments of other subjects. For the MTO studies, this assumption is not reasonable. Although little work has been done on the definition and estimation of treatment effects when interference is present, interference is common in studies of neighborhood effects and in many other social settings (e.g., schools and networks), and when data from such studies are analyzed under the “no-interference assumption,” very misleading inferences can result. Furthermore, ...read more
Citations
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Journal ArticleDOI
Matching Methods for Causal Inference: A Review and a Look Forward
TL;DR: A structure for thinking about matching methods and guidance on their use is provided, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
Posted Content
Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects
TL;DR: In this paper, the authors investigated conditions sufficient for identification of average treatment effects using instrumental variables and showed that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect.
Journal ArticleDOI
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available
Miguel A. Hernán,James M. Robins +1 more
TL;DR: This work outlines a framework for comparative effectiveness research using big data that makes the target trial explicit and channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
Journal ArticleDOI
Toward Causal Inference With Interference.
TL;DR: This article considers a population of groups of individuals where interference is possible between individuals within the same group, and proposes estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting.
Journal ArticleDOI
Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults
Jens Ludwig,Jens Ludwig,Greg J. Duncan,Lisa A. Gennetian,Lawrence F. Katz,Lawrence F. Katz,Ronald C. Kessler,Jeffrey R. Kling,Jeffrey R. Kling,Lisa Sanbonmatsu +9 more
TL;DR: Using data from Moving to Opportunity, a unique randomized housing mobility experiment, it is found that moving from a high-poverty to lower-p poverty neighborhood leads to long-term improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency.
References
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Journal Article
Identification of Causal effects Using Instrumental Variables
TL;DR: In this paper, a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable.
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Identification of Causal Effects Using Instrumental Variables
TL;DR: It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
Journal ArticleDOI
Community Structure and Crime: Testing Social-Disorganization Theory
TL;DR: In this article, a community-level theory that builds on Shaw and McKay's original model is formulated and tested, and the model is first tested by analyzing data for 238 localities in Great Britain constructed from a 1982 national survey of 10,905 residents.