We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem,...

In this paper we introduce the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in applications like average treatment effect estimation in observational studies. RRP is a Monte Carlo method that randomly generates non-empty recursive...

A new procedure to identify grading practice is proposed. In our approach, grading practice are given in terms of a categorical variable whilst usually in the literature, coefficients of the regression line which models school grades as a function of students' achievement, are taken as...

In this paper we describe some applications of the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in non parametric hot-deck missing data imputation, classification, prediction, average treatment effect estimation and, more generally, in...

We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal inference that satisfies several important in-sample properties. MIB generalizes and extends in several new directions the only existing class, ``Equal Percent Bias Reducing'' (EPBR), which is designed...

L'esame dell'archivio amministrativo di una delle maggiori società di fornitura di lavoro interinale consente di osservare le specificità dell'offerta di lavoro temporaneo, rimaste in ombra negli studi realizzati finora sul lavoro temporaneo in Italia. I dati esaminati sono relativi alla fase...

Purpose of this paper, which is a synthesis of a wider research project, is to show the impact of labour flexibility upon the employment structure of a prominent Italian region: Lombardy. The impact is especially investigated by focusing on the diffusion and the evolution of new "non standard"...