Minimax-regret treatment choice with missing outcome data

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Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis (2019, unknown): “, 2017) or elimination-by-aspects (Tversky, 1972), or regret-minimization (Manski, 2007).”
Minimax regret treatment choice with covariates or with limited validity of experiments (2012, methodology): “One recent focus of Charles Manski’s research is to apply statistical decision theory, and in particular the minimax regret criterion, to problems of treatment choice (Manski (2000, 2004, 2006, 2007a, 2007b, 2009); Brock and Manski (2008)).”
Partial Identification in Econometrics (2010, background): “Horowitz and Manski (1998; 2000) and Zaffalon (2002) study nonparametric prediction when nothing is known about the missingness process; Horowitz et al. (2003) and Horowitz and Manski (2005) consider the computationally challenging problem of parametric prediction.”
Statistical treatment choice based on asymmetric minimax regret criteria (2009, background): “Combining this result wtih (8) shows that for   max(1 3)”
Statistical decisions under ambiguity (2011, methodology): “This particular way of modeling ambiguity aversion via quasiconcavity is first found in Milnor (1954), was 9This statement comes with a technical caveat because the set of priors must be rich enough so that existence of certain objects in the proofs goes through.”