Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in...

Longitudinal studies on associations between changes in living environment and health are few and focus on movers. Next to causal effects, differences in health between living environments can, however, result due to residential mobility. The present study explored changes in living environment...

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex...

There is a large amount of empirical literature reporting that people who regularly attend religious services tend to have better health outcomes. However, it remains an unanswered question as to whether the observed correlation reflects any causality. Exploiting exogenous changes in church...

Abstract Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this...

Abstract Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects,...

Abstract In randomized cancer screening trials where asymptomatic individuals are assigned to undergo a regimen of screening examinations or standard care, the primary objective typically is to estimate the effect of screening assignment on cancer-specific mortality by carrying out an...

Abstract In this paper we present a continuous extension for longitudinal analysis settings of the recently proposed Covariate Balancing Propensity Score (CBPS) methodology. While extensions of the CBPS methodology to both marginal structural models and general treatment regimes have been...

Abstract In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in...

Understanding the mechanisms through which treatment effects come about is crucial for designing effective interventions. The identification of such causal mechanisms is challenging and typically requires strong assumptions. This paper discusses identification and estimation of natural direct...