Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data
Confirm that Clustering in Covariate X-space yields an "adjusted" LTD/...
Internal LocalControlStrategy functions.
Instrumental Variable LAO Fitting and Smoothing
Simulate a p-value for the significance of the Kolmogorov-Smirnov D-st...
Calculate the observed Distribution of LRCs in NU.Learning
Calculate the Observed Distribution of LTDs in NU.Learning
Create a <<Most-Like-Me>> data.frame for a specified X-Confounder vect...
Print Summary Statistics for One or More "Most-Like-Me" Histogram Pair...
NU.Learning: Nonparametric and Unsupervised Adjustment for Bias and Co...
Hierarchical Clustering of experimental units (such as patients) in X-...
Display NU Sensitivity Graphic for help in choice of K = Number of Clu...
Specify KEY parameters used in NU.Learning to "design" analyses of Obs...
Display an Instrumental Variable (LAO) plot with Linear and smooth.spl...
Display Visualizations of an Observed LRC Distribution in NU.Learning
Display Visualizations of an Observed LTD Distribution in NU.Learning
Display a Pair (or Pairs) of Histograms showing LOCAL effect-sizes for...
Print Summary Statistics on Local effect-size Estimates for Patients "...
Create a data.frame for use in Prediction of a LTD/LRC effect-size Dis...
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>.