When randomized experiments cannot be conducted in practice, propensity score (PS) techniques for matching treated and control units are frequently used for estimating causal treatment effects. Despite the popularity of PS techniques, they are not yet well studied for matching multilevel data where selection into treatment takes place at the lowest level. Two main strategies for matching level-...

This research examines whether lower-income married renters are more likely to become homeowners than comparable single renters. Using data from the Community Advantage Panel Study, we use discrete-time survival analysis with propensity score matching to explore this relationship. Results indicate married couples have higher odds of buying a home, and do so at faster rates, than their unmarried...

Instrumental variables (IVs) are used to control for confounding and measurement error in observational studies. They allow for the possibility of making causal inferences with observational data. Like propensity scores, IVs can adjust for both observed and unobserved confounding effects. Other methods of adjusting for confounding effects, which include stratification, matching and multiple reg...

Background: Lithium has been reported in some, but not all, studies to be associated with reduced suicide risk. Methods: Intention-to-treat, high-dimensional propensity score-matched cohort study of one-year suicide mortality in Veterans Health Administration patients (n = 21,194/treatment) initiating lithium or valproate from 1999-2008. One-year suicide mortality was examined among all patient...

Sensitivity of Propensity Score Methods to the Specifications Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we are incurring the curse-of-dimensional...

We use multiple longitudinal data sources and propensity score matching to assess the long-term outcomes of the Neighborhood Transformation Initiative (NTI) in the Sandtown-Winchester community of Baltimore. This comprehensive community initiative, implemented in the 1990s, remains one of the most well-known urban revitalization projects in the country, due to its significant funding (more than...

Propensity score matching and inverse-probability weighting are popular methods for causal inference in observational studies. Under the assumption of unconfoundedness, these methods enable researchers to estimate causal effects by balancing observed covariates across different treatment values. While their extensions to general treatment regimes exist, a vast majority of applications have been...

In this paper, we seek to evaluate the relative costs of debt for private versus comparable publicly traded rms. US studies of this important question have been limited due to the absence of comprehensive nancial data on privately-held rms. However, such data is available in the UK. Consequently, we employ a unique dataset of loans taken out by both types of UK rms with a large array of loan an...

The propensity score methods are widely used to adjust confounding effects in observational studies when comparing treatment effects. The propensity score is defined as the probability of treatment assignment conditioning on some observed baseline characteristics and it provides a balanced score for the treatment conditions as conditioning on the propensity score, the treatment groups are compa...

As labor market data is scarce in Sub-Saharan Africa (SSA), this paper uses household survey data to analyze the determinants of the gender gap in the labor market and its welfare implications for five SSA countries in multinomial logit models with propensity score matching method. The analysis confirms that education opens up opportunities for women to escape agricultural feminization and enga...