Abstract
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.
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Notes
- 1.
Results obtained by varying the number of observations (N = 250; N = 500), not reported here to save space, lead to the same conclusions.
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Conversano, C., Cannas, M., Mola, F. (2015). A Note on the Use of Recursive Partitioning in Causal Inference. In: Morlini, I., Minerva, T., Vichi, M. (eds) Advances in Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-17377-1_7
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DOI: https://doi.org/10.1007/978-3-319-17377-1_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17376-4
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