Abstract A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment effects. However, it is possible that a particular randomization yields covariate imbalances that...

Abstract Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical...

It has long been recognized that covariate adjustment can increase precision in randomized experiments, even when it is not strictly necessary. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved with even a...

Statistical methods have been developed for cost-effectiveness analyses (CEA) of cluster randomised trials (CRTs) where baseline covariates are balanced. However, CRTs may have systematic differences in individual and cluster-level covariates between the treatment groups. This paper presents...

When the maximum likelihood estimator is computationally inconvenient, covariate and Newton–Raphson adjustment often provide algebraically explicit yet still asymptotically efficient estimators. The bivariate normal correlation coefficient with known variances is used to show that these...

The accelerated failure time model is a useful alternative to the Cox proportional hazard model. We investigate whether or not a misspecified accelerated failure time model provides a valid test of the no-treatment effect in randomized clinical trials. We show that the minimum dispersion...

Parents invest in their children's human capital in several ways. We investigate the extent to which the levels and composition of parent-child time varies across countries with different welfare regimes: Finland, Germany and the United States. We test the hypothesis of parentchild time as a...

In this paper, we investigate what can be learned about average counterfactual outcomes when it is assumed that treatment response functions are smooth. The smoothness conditions in this paper amount to assuming that the differences in average counterfactual outcomes are bounded under different...