Treatment effect heterogeneity in paired data

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Estimating treatment effect heterogeneity in randomized program evaluation (2013, background): “…Zhu and Murphy (2011), Qian and Murphy (2011)], (3) testing the existence or lack of heterogeneous treatment effects [e.g., Gail and Simon (1985), Davison (1992), Crump et al. (2008)], and (4) generalizing causal effect estimates obtained from an experimental sample to a target population [e.g.,…”
Estimation of generalized linear latent variable models (2004, methodology): “This idea has been used in other models, e.g. by Davison (1992).”
Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control (2011, methodology): “Using (9), we may use algorithms by Imhof (1961) and by Robbins & Pitman (1949). We found that Imhof’s algorithm is generally faster, but Robbins’ algorithm can be numerically more accurate for the very small p-values that are of interest in genomics research.”