Instrumental Variables Estimation
Jump to search In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.[1] Intuitively, IV is used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA gives biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable. Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur 1) when changes in the dependent variable change the value of at least one of the covariates (‘reverse’ causation), 2) when there are omitted variables that affect both the dependent and independent variables, or 3) when the covariates are subject to non-random measurement error. Explanatory variables which suffer from one or more of these issues in the context of a regression are sometimes referred to as endogenous. In this situation, ordinary least squares produces biased and inconsistent estimates.[2] However, if an instrument is available, consistent estimates may still be obtained. An instrument is a variable that does not itself belong in the explanatory equation but is correlated with the endogenous explanatory variables, conditional on the value of other covariates. …

SimDex
We present SimDex, a new technique for serving exact top-K recommendations on matrix factorization models that measures and optimizes for the similarity between users in the model. Previous serving techniques presume a high degree of similarity (e.g., L2 or cosine distance) among users and/or items in MF models; however, as we demonstrate, the most accurate models are not guaranteed to exhibit high similarity. As a result, brute-force matrix multiply outperforms recent proposals for top-K serving on several collaborative filtering tasks. Based on this observation, we develop SimDex, a new technique for serving matrix factorization models that automatically optimizes serving based on the degree of similarity between users, and outperforms existing methods in both the high-similarity and low-similarity regimes. SimDexfirst measures the degree of similarity among users via clustering and uses a cost-based optimizer to either construct an index on the model or defer to blocked matrix multiply. It leverages highly efficient linear algebra primitives in both cases to deliver predictions either from its index or from brute-force multiply. Overall, SimDex runs an average of 2x and up to 6x faster than highly optimized baselines for the most accurate models on several popular collaborative filtering datasets. …

littler (“little R”)
littler provides the r program, a simplified command-line interface for GNU R. This allows direct execution of commands, use in piping where the output of one program supplies the input of the next, as well as adding the ability for writing hash-bang scripts, i.e. creating executable files starting with, say, #!/usr/bin/r.
GNU R, a language and environment for statistical computing and graphics, provides a wonderful system for ‘programming with data’ as well as interactive exploratory analysis, often involving graphs. Sometimes, however, simple scripts are desired. While R can be used in batch mode, and while so-called here documents can be crafted, a long-standing need for a scripting front-end has often been expressed by the R Community. littler (pronounced little R and written r) aims to fill this need. …