| Name : R-NU.Learning
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| Version : 1.5
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.1.5
| Date : 2024-06-14 11:02:37
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| Group : Development/Libraries/Other
| Source RPM : R-NU.Learning-1.5-lp153.1.5.src.rpm
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| Size : 2.14 MB
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| Packager : (none)
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| Summary : Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data
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Description :
Especially when cross-sectional data are observational, effects of
treatment selection bias and confounding are best revealed by using
Nonparametric and Unsupervised methods to \"Design\" the analysis of the
given data ...rather than the collection of \"designed data\".
Specifically, the \"effect-size distribution\" that best quantifies a
potentially causal relationship between a numeric y-Outcome variable
and either a binary t-Treatment or continuous e-Exposure variable needs
to consist of BLOCKS of relatively well-matched experimental units
(e.g. patients) that have the most similar X-confounder
characteristics. Since our NU Learning approach will form BLOCKS by
\"clustering\" experimental units in confounder X-space, the implicit
statistical model for learning is One-Way ANOVA. Within Block measures
of effect-size are then either [a] LOCAL Treatment Differences (LTDs)
between Within-Cluster y-Outcome Means (\"new\" minus \"control\") when
treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs)
when the e-Exposure variable is numeric with (hopefully many) more than
two levels. An Instrumental Variable (IV) method is also provided so
that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
information for effect-size inferences when X-Covariates are assumed to
influence Treatment choice or Exposure level but otherwise have no
direct effects on y-Outcomes. Finally, a \"Most-Like-Me\" function
provides histograms of effect-size distributions to aid Doctor-Patient
(or Researcher-Society) communications about Heterogeneous Outcomes.
Obenchain and Young (2013) < doi:10.1080/15598608.2013.772821>
Obenchain, Young and Krstic (2019) < doi:10.1016/j.yrtph.2019.104418>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
