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Let $R_i=1$ indicate an observed outcome. Fitting the conditional outcome model to responders requireswith a positive observation probability over the relevant predictor support. This is conditional missing at random for the predictors used in the regression: after conditioning on them, observation must not further select participants by their unobserved outcome. It gives $f(Y_i\mid x_i,Z_i,R_i=1)=f(Y_i\mid x_i,Z_i)$, the complete-case regression under conditional missing at random condition. Correct specification of the conditional mean model is also required. Missing completely at random is sufficient but stronger; simply assuming missingness depends only on a post-randomization variable excluded from this model would not suffice.