Principal Stratification Approach to Broken Randomized Experiments

By J. Barnard, C. Frangakis, Jennifer L. Hill, Donald B. Rubin. 2003

217 citations recorded by the source corpus.

Subfields: Education, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0fc5bbf4c79815cbcbf1b949a3353665e2b50b46

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Citations in the corpus

A Potential Outcomes View of Value-Added Assessment in Education (2004, background): “…if the missing data are missing completely at random (MCAR, Little & Rubin, 2002) and is not appropriate in settings such as this with longitudinal data and missing outcomes (see, e.g., Barnard et al., 1998, 2003), even when the missing data are ignorable (Rubin, 1976c; Little and Rubin, 2002).”
Augmented designs to assess principal strata direct effects (2011, methodology): “Various (full and partial) identification strategies and estimation methods for principal effects have been developed (e.g. Barnard et al. (2003), Cheng and Small (2006), Cheng et al. (2009), Frangakis et al. (2007), Gallop et al. (2009), Imai (2008), Lee (2009), Lynch et al. (2008), Mattei and…”
Simple relations between principal stratification and direct and indirect effects (2008, background): “Pearl (2001) gave definitions for controlled direct effects and natural direct and indirect effects based on interventions on themediator or intermediate variable Z . Robins and Greenland (1992) provided related definitions. The controlled direct effect of treatment A on outcome Y comparing A = a wi”
Latent class instrumental variables: A clinical and biostatistical perspective (2015, background): “Other names are ‘the IV estimand embedded in the Rubin Causal Model’ [9], ‘principal stratification approach to broken randomized experiments’ [10], ‘modern instrumental variables literature’ [11], ‘IV assumptions and estimation for binary IV and binary treatment’ [12], and ‘instrumental variable an”
The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods (2008, background): “, compliance and missing data problems), but the fact that there was a manipulation can be very helpful in correcting the problems—Barnard et al. (2003). Corrections are more problematic in the absence of an experimental manipulation because additional assumptions are required.”
School Vouchers and Student Outcomes: Experimental Evidence from Washington, DC (2013, background): “Another analysis found program-induced gains, but only for African-Americans in math (Barnard et al., 2003).”