  This paper proposes an empirical method to implement the recentered influence
function (RIF) regression of Firpo, Fortin and Lemieux (2009), a relevant
method to study the effect of covariates on many statistics beyond the mean. In
empirically relevant situations where the influence function is not available
or difficult to compute, we suggest to use the \emph{sensitivity curve} (Tukey,
1977) as a feasible alternative. This may be computationally cumbersome when
the sample size is large. The relevance of the proposed strategy derives from
the fact that, under general conditions, the sensitivity curve converges in
probability to the influence function. In order to save computational time we
propose to use a cubic splines non-parametric method for a random subsample and
then to interpolate to the rest of the cases where it was not computed. Monte
Carlo simulations show good finite sample properties. We illustrate the
proposed estimator with an application to the polarization index of Duclos,
Esteban and Ray (2004).
