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Source: Annals of Applied Statistics
Resulting in 2 citations.
1. Hill, Jennifer L.
Su, Yu-Sung
Assessing Lack of Common Support in Causal Inference Using Bayesian Nonparametrics: Implications for Evaluating the Effect of Breastfeeding on Children's Cognitive Outcomes
Annals of Applied Statistics 7,3 (September 2013): 1386-1420.
Also: https://projecteuclid.org/journals/annals-of-applied-statistics/volume-7/issue-3
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Institute of Mathematical Statistics
Keyword(s): Breastfeeding; Cognitive Development; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there may exist neighborhoods of the covariate space where there are not sufficient numbers of both groups of women (those who breastfed for prolonged periods and those who did not) to make inferences about those women. This is referred to as lack of common support. Problems can arise when we try to estimate causal effects for units that lack common support, thus we may want to avoid inference for such units. If ignorability is satisfied with respect to a set of potential confounders, then identifying whether, or for which units, the common support assumption holds is an empirical question. However, in the high-dimensional covariate space often required to satisfy ignorability such identification may not be trivial. Existing methods used to address this problem often require reliance on parametric assumptions and most, if not all, ignore the information embedded in the response variable. We distinguish between the concepts of "common support" and "common causal support." We propose a new approach for identifying common causal support that addresses some of the shortcomings of existing methods. We motivate and illustrate the approach using data from the National Longitudinal Survey of Youth to estimate the effect of breastfeeding at least nine months on reading and math achievement scores at age five or six. We also evaluate the comparative performance of this method in hypothetical examples and simulations where the true treatment effect is known.
Bibliography Citation
Hill, Jennifer L. and Yu-Sung Su. "Assessing Lack of Common Support in Causal Inference Using Bayesian Nonparametrics: Implications for Evaluating the Effect of Breastfeeding on Children's Cognitive Outcomes." Annals of Applied Statistics 7,3 (September 2013): 1386-1420.
2. Schofield, Lynne Steuerle
Correcting for Measurement Error in Latent Variables Used as Predictors
Annals of Applied Statistics 9,4 (December 2015): 2133-2152.
Also: http://www.ncbi.nlm.nih.gov/pubmed/26977218
Cohort(s): NLSY97
Publisher: Institute of Mathematical Statistics
Keyword(s): College Major/Field of Study/Courses; Methods/Methodology; Modeling, Mixed Effects; Modeling, Structural Equation; Personality/Big Five Factor Model or Traits; Test Scores/Test theory/IRT

Permission to reprint the abstract has not been received from the publisher.

This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I find prior mathematics proficiency and personality have been previously underestimated in the STEM retention literature. Pre-college mathematics proficiency and personality explain large portions of the racial and gender gaps. The findings have implications for those who design interventions aimed at increasing the rates of STEM persistence among women and under-represented minorities.
Bibliography Citation
Schofield, Lynne Steuerle. "Correcting for Measurement Error in Latent Variables Used as Predictors." Annals of Applied Statistics 9,4 (December 2015): 2133-2152.