Generalized Correlations and Kernel Causality Using R Package GeneralCorr

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) argues that kernel causality amounts to model selection between two kernel regressions, E(Y|X) = g1(X) and E(X|Y) = g2(Y) and reports simulations favoring kernel causality. An R software package called 'generalCorr' computes generalized 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 quantfication to extensive causality apparatus of Pearl (2010) and additive-noise type methods in Mooij et al. (2014), who seem to offer no R implementations. 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