A tree-based approach for identification of a balanced group of observations in causal inference studies is presented. The method uses an algorithm based on a multidimensional balance measure criterion applied to the values of the covariates to recursively split the data. Starting from an ad-hoc resampling scheme, observations are finally partitioned in subsets characterized by different degrees of homogeneity, and causal inference is carried out on the most homogeneous subgroups.
CITATION STYLE
Conversano, C., Cannas, M., & Mola, F. (2015). A note on the use of recursive partitioning in causal inference. In Advances in Statistical Models for Data Analysis (pp. 55–62). Springer International Publishing. https://doi.org/10.1007/978-3-319-17377-1_7
Mendeley helps you to discover research relevant for your work.