We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R’s numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.

A new matching method is proposed for the estimation of the average treatment e#ect of social policy interventions (e.g., training programs or health care measures). Given an outcome variable, a treatment and a set of pre-treatment covariates, the method is based on the examination of random recursive partitions of the space of covariates using regression trees. A regression tree is grown either on the treated or on the untreated individuals only using as response variable a random permutation of the indexes 1. . . n (n being the number of units involved), while the indexes for the other group are predicted using this tree. The procedure is replicated in order to rule out the e#ect of specific permutations. The average treatment e#ect is estimated in each tree by matching treated and untreated in the same terminal nodes. The final estimator of the average treatment e#ect is obtained by averaging on all the trees grown. The method does not require any specific model assumption apart from the tree's complexity, which does not a#ect the estimator though. We show that this method is either an instrument to check whether two samples can be matched (by any method) and, when this is feasible, to obtain reliable estimates of the average treatment e#ect. We further propose a graphical tool to inspect the quality of the match. The method has been applied to the National Supported Work Demonstration data, previously analyzed by Lalonde (1986) and others.

Average treatment effect estimation via random recursive partitioning