Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's K[superscript 2], and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if: (a) the regression error in population is normal; (b) an unobserved explanatory variable correlates with any variables equal to or less than 0.2; (c) a curvilinear relation between both variables is not strong (standardized regression coefficient less than or equal to 0.2); (d) there are no bivariate outliers; and (e) both variables are continuous. (Contains 3 tables and 2 figures.)