Treatment effects beyond the mean using GAMLSS

Abstract

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.