Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of the propensity scores in a probabilistic sense. We do a simple...

Funded by collage st. innovative projects Received: January 21, 2011 Accepted: February 10, 2011 doi:10.5539/jmr.v3n3p52 Abstract Causal inferences on the average treatment effect in observational studies are always difficult problems because the distributions of samples in the two treatment groups can not be observed at the same time, and the estimation of the treatment effect is often biased....

The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate match...

Foundation and a grant from the Huntsman Center at the Wharton School. The opinions expressed are those of the authors and do not necessarily reflect the views of the research sponsors. The views expressed herein are those of the author(s) and not necessarily those of the National Bureau of Economic Research. ABSTRACT This paper examines the determinants of M&A activity in the pharmaceutical-bi...

Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, one must model the distribution of the treatment indicator given a vector of covariates. Much of work has been done in the case of covariates that are fully observed. Many studies, such as longitudinal surveys, suffer from missing covariate. In this paper, different approaches nam...

Propensity score based-estimators are commonly used to estimate causal effects in evaluation research. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probabili...

This paper presents evidence of both counter-cyclical and secular decline in the union membership wage premium in the US and the UK over the last couple of decades. The premium has fallen for most groups of workers, the main exception being public sector workers in the US. By the beginning of the 21 Century the premium remained substantial in the US but there was no premium for many workers in ...

This paper examines the extent to which the relationship between leaving home and entry into poverty among young people is causal: that is, how far poverty entry is the result of leaving home, rather than arising from heterogeneity or selection. Using propensity score matching, we estimate the effect of home-leaving on entry into poverty and deprivation, with data from the European Community Ho...

The authors provide evidence on the impact of foreign ownership on labor market outcomes analyzing pay differences between foreign-acquired and domestically owned firms. For this purpose, they use firm-level data from 16 European countries over the time period 1999–2006. Combing propensity score matching techniques with difference-in-differences (DID) estimators the authors estimate positive wa...

Individuals’ pro-social behaviors are driven by altruistic and selfish motivations. In this paper we explore how the introduction of external incentives would influence one’s pro-social behavior both in the short term and in the long run. Using a large data set on Amazon product reviews, we design a quasiexperimental approach where we combine a propensity score matching (PSM) and a difference-i...