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...

In observational studies the overall aim when fitting a model for the propensity score is to reduce bias for an estimator of the causal effect. For this purpose guidelines for covariate selection for propensity score models have been proposed in the causal inference literature. To make the...

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...

In observational studies the overall aim when fitting a model for the propensity score is to reduce bias for an estimator of the causal effect. For this purpose guidelines for covariate selection for propensity score models have been proposed in the causal inference literature. To make the...

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...

In this paper, we show that a two-component normal mixture model provides a good approximation to the logistic distribution. This model is an improvement over using the normal distribution and is comparable with using the t-distribution as approximating distributions. The result from using the...

In the causal inference literature a class of semi-parametric estimators is called robust if the estimator has desirable properties under the assumption that at least one of the working models is correctly specified. A standard example is a doubly robust estimator that specifies parametric...

Abstract Objectives Spurious associations between an exposure and outcome not describing the causal estimand of interest can be the result of selection of the study population. Recently, sensitivity parameters and bounds have been proposed for selection bias, along the lines of sensitivity...

This article develops a theoretical model for evaluating mandatory activation of welfare recipients in complex activation programmes. The model aims to summarize and describe heterogeneous content that is difficult to comprehend because of local variations, staff characteristics, or other...

An estimand of interest in empirical studies with observational data is the average treatment effect of a multi-valued treatment in the treated subpopulation. We demonstrate three estimation approaches: outcome regression, inverse probability weighting and inverse probability weighted...