We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting...

In observational studies, investigators usually do not have the same control over the treatment assignment as they do with randomized controlled studies. As a result, the treatment and control groups may have a large difference on their observed covariates. These differences could lead to bias in estimating treatment effects. There are several propensity score based methods that could reduce th...

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment o the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting s...

The Effect of Breastfeeding on Children’s Cognitive and Noncognitive Development This paper uses propensity score matching methods to investigate the relationship between breastfeeding and children’s cognitive and noncognitive development. We find that breastfeeding for four weeks is positively and statistically significantly associated with higher cognitive test scores, by around one tenth of ...

The aim is to assess the effect of imprisonment on rape myth acceptance. The research used a sample of male prisoners incarcerated for non-sexual crimes (n = 98) and a sample of males drawn from the general population (n = 160). Simple linear regression did not indicate a significant effect of incarceration on rape myth acceptance. After controlling for background covariates using propensity sc...

Observational studies draw inferences about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. Let r1(x) be the response when a unit with covariates x receives the treatment (z = 1), and r0(x) be the response when that unit does not receive the treatment (z = 0). Then one is int...

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficien...

The use of propensity score methods for program evaluation with non-experimental data typically requires the propensity score be estimated, often with a model whose specification is unknown. While theoretical results suggest that estimators utilizing more flexible propensity score specifications perform better, this has not filtered into applied research. Here, we provide Monte Carlo evidence i...

In this paper we characterise the selection into parenthood for men and women separately and estimate effects of motherhood and fatherhood on wages. We apply propensity score matching exploiting an extensive high-quality register-based data set augmented with family background information. We estimate net effects of parenthood and find that mothers receive 7.4% lower average wages compared to n...

The use of propensity score methods (Rosenbaum and Rubin, 1983) have become popular for estimating causal inferences in observational studies in medical research (Austin, 2008) and in the social sciences (Thoemmes and Kim, 2011). In most cases however, the use of propensity score methods have been confined to a single treatment. Several researchers have suggested using propensity score methods ...