Generalized Correlations and Kernel Causality Using R Package generalCorr Hrishikesh D. Vinod* October 3, 2017 Abstract Karl Pearson developed the correlation coefficient r(X,Y) in 1890's. Vinod (2014) develops new generalized correlation coefficients so that when r∗(Y X) > r∗(X Y) then X is the \kernel cause" of Y. Vinod (2015a) arguesj that kernelj causality amounts to model selection be- tween two kernel regressions, E(Y X) = g1(X) and E(X Y) = g2(Y) and reports simulations favoring kernelj causality. An Rj software pack- age called `generalCorr' (at www.r-project.org) computes general- ized correlations, partial correlations and plausible causal paths. This paper describes various R functions in the package, using examples to describe them. We are proposing an alternative quantification to extensive causality apparatus of Pearl (2010) and additive-noise type methods in Mooij et al. (2014), who seem to offer no R implementa- tions. My methods applied to certain public benchmark data report a 70-75% success rate. We also describe how to use the package to assess endogeneity of regressors. Keywords: generalized measure of correlation, non-parametric regres- sion, partial correlation, observational data, endogeneity. *Vinod: Professor of Economics, Fordham University, Bronx, New York, USA 104 58. E-mail:
[email protected]. A version of this paper was presented at American Statistical Association's Conference on Statistical Practice (CSP) on Feb. 19, 2016, in San Diego, California, and also at the 11-th Greater New York Metro Area Econometrics Colloquium, Johns Hopkins University, Baltimore, Maryland, on March 5, 2016. I thank Dr Pierre Marie Windal of Paris for pointing out an error in partial correlation coefficients.