Estimation of Intervention Effects with Noncompliance: Alternative Model Specifications

By Booil Jo. 2002

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In Statistics and Probability

Citations in the corpus

Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification (2008, background): “The two mentioned strategies for dealing with missing data in the covariates (namely, deleting individuals and defining additional cells) yield valid inferences under different assumptions on the missing mechanism, as discussed by Mattei (2004). To study the dependence of the bounds on the covariate”
Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance (2013, background): “This is particularly true in open-label experiments, which are the norm in the social sciences (e.g., Jo 2002; Duflo, Glennerster, and Kremer 2008), in randomized encouragement studies (Hirano et al. 2000), and in observational studies with IV (e.g., Angrist Fabrizia Mealli is Professor, Department…”
School Success, Possible Selves, and Parent School Involvement* (2007, methodology): “…is appropriate if two basic requirements are met: (a) assessed variables provide a useful and valid estimate of those likely to participate and (b) nonparticipators among those assigned to the experimental condition do not differ in outcomes from control condition youth (Jo, 2002a, 2002b).”
A Likelihood-Based Analysis for Relaxing the Exclusion Restriction in Randomized Experiments with Imperfect Compliance (2013, methodology): “…to achieve complete relaxation of the assumption, the current study employs a likelihood estimation approach, which is known to be more efficient than the IV framework for the identification and estimation of causal effects for compliers (Imbens and Rubin, 1997a; Little and Yau, 1998; Jo, 2002).”
Possible selves and academic outcomes: How and when possible selves impel action. (2006, background): “…received a meaningful dose of an intervention is appropriate if assessed variables provide a useful and valid estimate of those likely to participate and if nonparticipators among those assigned to the experimental condition do not differ in outcomes from control condition youth (Jo, 2002a, 2002b).”
Identification of Causal Parameters in Randomized Studies With Mediating Variables (2008, background): “subjects. Jo (2002a, 2002b) weakens this assumption and also shows how covariate information”
General growth mixture modeling for randomized preventive interventions. (2001, methodology): “The model can be estimated by maximum-likelihood using EM algorithms. Muthén and Shedden (1999) proposed an EM algorithm drawing on (14), while Muthén and Muthén (2001) use an EM algorithm drawing on (15).”