I provide a simple introduction to the use of propensity score reweighting to assess the effect of changes in covariates on the distribution of an outcome (such as wages). I relate this to the literature on estimating “average treatment effects” and Blinder/Oaxaca decompositions as well as discuss some of the limitations and uses of reweighting.

Comparative-effectiveness research (CER) aims to produce actionable evidence regarding the effectiveness and safety of medical products and interventions as they are used outside of controlled research settings. Although CER evidence regarding medications is particularly needed shortly after market approval, key methodological challenges include (i) potential bias due to channeling of patients ...

We analyze the wage effects of employment breaks of women entering motherhood using a novel within-firm matching approach where mothers’ wages upon return to the job are compared with those of their female colleagues. We investigate three different matching procedures: (1) exact matching on individual characteristics two years before birth, (2) propensity score matching and (3) a combined proce...

OBJECTIVE To provide a tutorial for using propensity score methods with complex survey data. DATA SOURCES Simulated data and the 2008 Medical Expenditure Panel Survey. STUDY DESIGN Using simulation, we compared the following methods for estimating the treatment effect: a naïve estimate (ignoring both survey weights and propensity scores), survey weighting, propensity score methods (nearest ...

BACKGROUND Few models of how community mobilisation works have been elaborated in the scientific literature, and evaluation of the impact of these programmes on HIV and other health outcomes is extremely limited. Avahan, the India AIDS Initiative, has been implementing community mobilisation as part of its prevention programming with groups of high-risk individuals across six states since 2005....

Propensity-score matching is increasingly being used to estimate the effects of treatments using observational data. In many-to-one (M:1) matching on the propensity score, M untreated subjects are matched to each treated subject using the propensity score. The authors used Monte Carlo simulations to examine the effect of the choice of M on the statistical performance of matched estimators. They...

In individually randomised controlled trials, adjustment for baseline characteristics is often undertaken to increase precision of the treatment effect estimate. This is usually performed using covariate adjustment in outcome regression models. An alternative method of adjustment is to use inverse probability-of-treatment weighting (IPTW), on the basis of estimated propensity scores. We calcula...

We first define some notation. Let Y denote the response of interest and X denote a p-dimensional vector of covariates. Let T denote a binary indicator of treatment exposure: T = 1 if treated, T = 0 if control. (Yi,Xi, Ti), i = 1, . . . , n, is a random sample from (Y,X, T ). We further define Y (1) as the potential outcome if the subject were treated and Y (0) as the potential outcome if the s...

OBJECTIVES Caring for grandchildren is a common and normative experience for many Chinese grandparents. This study investigates the influence of child care provision on older adults' health trajectories in China. METHOD Using data from the China Health and Nutrition Survey (1991, 1993, 1997, 2000, 2004, and 2006), we apply growth curve models to examine the effect of living arrangements and i...

In cancer observational studies, differences between groups on confounding variables may have a significant effect on results when examining health outcomes. This study demonstrates the utility of propensity score matching to balance a non-cancer and cancer cohort of older adults on multiple relevant covariates. This approach matches cases to controls on a single indicator, the propensity score...