MoCo is a multiple-robust nonparametric estimator for the motion-standardized difference in functional connectivity between autistic and non-ASD children that uses an ensemble of machine learning methods on all participants.
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A semiparametric method using conditional influence functions and local linear regression is proposed to estimate CATE functions in the target population from nested trial data.
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Nonparametric Motion Control in Functional Connectivity Studies in Children with Autism Spectrum Disorder
MoCo is a multiple-robust nonparametric estimator for the motion-standardized difference in functional connectivity between autistic and non-ASD children that uses an ensemble of machine learning methods on all participants.
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Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
A semiparametric method using conditional influence functions and local linear regression is proposed to estimate CATE functions in the target population from nested trial data.