Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey
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© Institute of Mathematical Statistics, 2014.Motivated by recent findings in the field of consumer science, this paper evaluates the causal effect of debit cards on household consumption using population-based data from the Italy Survey on Household Income and Wealth (SHIW). Within the Rubin Causal Model, we focus on the estimand of population average treatment effect for the treated (PATT). We consider three existing estimators, based on regression, mixed matching and regression, propensity score weighting, and propose a new doubly-robust estimator. Semiparametric specification based on power series for the potential outcomes and the propensity score is adopted. Cross-validation is used to select the order of the power series. We conduct a simulation study to compare the performance of the estimators. The key assumptions, overlap and unconfoundedness, are systematically assessed and validated in the application. Our empirical results suggest statistically significant positive effects of debit cards on the monthly household spending in Italy.
Published Version (Please cite this version)10.1214/14-AOAS784
Publication InfoMercatanti, A; & Li, F (2014). Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey. Annals of Applied Statistics, 8(4). pp. 2485-2508. 10.1214/14-AOAS784. Retrieved from https://hdl.handle.net/10161/10303.
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Professor of Statistical Science
My main research interest is causal inference and its applications to health, policy and social science. I also work on the interface between causal inference and machine learning. I have developed methods for propensity score, clinical trials, randomized experiments (e.g. A/B testing), difference-in-differences, regression discontinuity designs, representation learning. I also work on Bayesian analysis and statistical methods for missing data. I am serving as the editor for social science, bios
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