  This paper introduces distributional regression, also known as generalized
additive models for location, scale and shape (GAMLSS), as a modeling framework
for analyzing treatment effects beyond the mean. By relating each parameter of
the response distribution to explanatory variables, GAMLSS model the treatment
effect on the whole conditional distribution. Additionally, any nonnormal
outcome and nonlinear effects of explanatory variables can be incorporated. We
elaborate on the combination of GAMLSS with program evaluation methods in
economics and provide practical guidance on the usage of GAMLSS by reanalyzing
data from the Mexican \textit{Progresa} program. Contrary to expectations, no
significant effects of a cash transfer on the conditional inequality level
between treatment and control group are found.
