Propensity Score in Multilevel Data
Impact assessment, Propensity Score, Matching in multilevel data
Recently there has been an increase in the use of methodology involving propensity score to minimize selection bias in observational studies which the aim is to observe causal relationships. The definition of propensity score is the probability one has to receive a treatment, given the covariates measured at the baseline. Also, a propensity score can be used to adjust treatment effect through pairing or weighing, using the inverse of this probability. Propensity score matching demands several implementation steps such as propensity score estimation, selecting a matching algorithm, and evaluating the intervention effects. A great portion of researches that use propensity score as an approach, assumes independence of observations. However, in studies of many fields of knowledge such as education, social science, and even health, the research design has a hierarchical structure with aggregated individuals. When it comes to multilevel structured data, the propensity score estimation must take into account the effect different levels have on an observation. For this multilevel context, the quality os the matching between the treated and control group and the estimate of the treatment were evaluated through a simulation study proceded from different models for the propensity score estimation. At last, the methodology used in this paper was applied to a real data set exploring the effect of the Bolsa Família program on nutritional status through BMI in a multilevel household survey conducted in Camaçari - Bahia, from October 2011 to January 2012.