What Do Randomized Studies of Housing Mobility Demonstrate?

By Michael E. Sobel. 2006

560 citations recorded by the source corpus.

Subfields: Education, Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b7ece50657603b282e8df747d63753ed9df7cf37

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Citations in the corpus

Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference (2017, methodology): “Many methods rely on the assumption of partial interference (Sobel, 2006), i.e., that subjects can be partitioned into groups such that a subject in one group may not interfere with a subject in another.”
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? (2019, unknown): “In contrast, our work builds upon a small literature on the use of linear fixed effects models for causal inference with longitudinal data in econometrics and statistics (e.g., Wooldridge, 2005a; Sobel, 2006; Arkhangelsky and Imbens, 2018).”
Causal inference with misspecified exposure mappings: separating definitions and assumptions (2021, background): “This initial work was later developed by Sobel (2006) and Hudgens and Halloran (2008), who both consider effects that we today would recognize as exposure effects.”
Causal Inference for Time-Varying Instructional Treatments (2008, background): “Recent work has extended Rubin’s causal model by invoking weaker and relatively more plausible assumptions that support causal inference in multilevel settings in a single time period (Gitelman, 2005; Hong & Raudenbush, 2006; Sobel, 2006).”
Estimating Average Causal Effects Under General Interference (2012, background): “Sobel (2006) analyzes the potential for bias in such settings when non-interference is mistakenly assumed, and then defines a number of direct and indirect effects that, in principle, may be identifiable.”
Matching methods for causal inference: A review and a look forward (2010, background): “Recent work has also begun thinking about how to relax this assumption in analyses (Hong and Raudenbush, 2006; Sobel, 2006; Hudgens and Halloran, 2008).”
Identification and Estimation of Spillover Effects in Randomized Experiments (2017, background): “Interference can occur between units in the same group, but not between units in different groups, an assumption sometimes known as partial interference (Sobel, 2006).”
Estimating Average Causal Effects Under Interference Between Units (2013, background): “Consistency for the variance estimators, asymptotic normality and therefore conﬁdence intervals follow straightforwardly when the growth process involves designs and exposure mappings that are independent across subpopulations, implying partial interference (Sobel, 2006).”
Large sample randomization inference of causal effects in the presence of interference (2014, background): “In econometric studies, interference may be present between households in the same neighborhood (Sobel 2006) or other settings where individuals interact socially (Manski 2012).”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “Finally, we did not consider target trials with interference (52 – 54) or crossover, and we ignored effects that may result from the scaling up of the treatment strategies outside of the studied populations.”
Causal diagrams for interference (2014, background): “This may be justified if the blocks are separated by time or space (Hudgens and Halloran, 2008; Sobel, 2006; Tchetgen Tchetgen and VanderWeele, 2012; Rosenbaum, 2007).”