Inference for the Complier-Average Causal Effect From Longitudinal Data Subject to Noncompliance and Missing Data, With Application to a Job Training Assessment for the Unemployed

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A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “With a binary exposure and a single IV, under the assumption of monotonicity in the effect of the IV on the exposure, the causal estimand from an IV analysis is known as a local average treatment effect(27) or (in particular in the case of a binary IV) a complier-averaged causal effect.(28) It repre”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “Little and Yau (1998) and Yau and Little (2001) similarly model the conditional expectation of the outcome given compliance status and covariates.”
Regression and Weighting Methods for Causal Inference Using Instrumental Variables (2006, methodology): “Barnard, Frangakis, Hill, and Rubin (2003), Frangakis et al. (2004), and Yau and Little (2001) extended the methods to allow an ordinal instrument and handle missing data.”
ESTIMATION IN AN INSTRUMENTAL VARIABLES MODEL WITH TREATMENT EFFECT HETEROGENEITY (2012, methodology): “When the instrument is binary, Abadie (2003) works out a semi-parametric approach to approximating a treatment response function, and Hirano, Imbens, Rubin and Zhou (2000) and Yau and Little (2001) use a parametric approach to estimate a late that does not condition on covariates.”