Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are revealed by using the Nonparametric and Unsupervised "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is to estimate the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and a t-Treatment or e-Exposure variable. Treatment variables are binary {either 1 = "new" or 0 = "control"}, while Exposure variables vary continuously over a finite range. LC Strategy starts by CLUSTERING experimental units (individual patients, US Counties, etc.) on their X-confounder characteristics. Clusters represent exclusive and exhaustive BLOCKS of relatively well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA. Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure is numeric with (many) more than two levels. Otherwise, Treatment choice is Nested within BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ["new" minus "control"]. 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 ...assuming that X-Covariates influence only Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient or Researcher-Society communications about Heterogeneous Outcomes.