We conduct research on issues related to causal inference in observational studies. We examine how propensity score methods can be applied in nonstandard settings (multilevel dataset, longitudinal data, neighborhood studies). We are also interested in the development and use of machine learning techniques for causal inference and social science studies.
Arpino, B., Le Moglie, M., and L. Mencarini (2018). Machine-Learning techniques for family demography: An application of random forests to the analysis of divorce determinants in Germany. RECSM Working Paper 56. Access to the article
Cannas M. and B. Arpino (2018). Machine learning techniques for propensity score matching and weighting. RECSM Working Paper 54. Access to the article
Arpino B., De Benedictis L. and A. Mattei (2017).Implementing Propensity Score Matching with Network Data: The effect of GATT on bilateral trade. Journal of the Royal Statistical Society - C 66(3), 537–554.
Arpino B., and M. Cannas (2016). Propensity score matching with clustered data. An application to the estimation of the impact of caesarean section on the Apgar score. Statistics in Medicine 35(12), 2074–2091.
Arpino B., and A. Mattei (2016). Assessing the Causal Effects of Financial Aids to Firms in Tuscany allowing for Interference. The Annals of Applied Statistics 10(3), 1170-1194.