A review of epidemiological papers conducted in 2009 concluded that several studies
employed variable selection methods susceptible to introduce bias and yield inadequate …

It is well-known that the convergence rates of nonparametric efficiency estimators (eg, free-
disposal hull and data envelopment analysis estimators) become slower with increasing …

A randomized trial allows estimation of the causal effect of an intervention compared to a
control in the overall population and in subpopulations defined by baseline characteristics …

To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed.
If there is sufficient knowledge on the underlying causal structure then existing confounder …

This paper investigates the appropriateness of the integration of flexible propensity score
modeling (nonparametric or machine learning approaches) in semiparametric models for …

In order to ensure a good start to life for every child there is a need for research on social
determinants of health and, in wider social and economic spheres, to achieve greater health …

This paper designs a systemic framework to quantify speed reduction induced by traffic
incidents using a causal inference framework. The results can provide a reference to traffic …

Evaluations of agri-environmental schemes (AES) based on observational farm data
generally use a matching algorithm for comparing participating and non-participating farms …

To estimate causal treatment effects, we propose a new matching approach based on the
reduced covariates obtained from sufficient dimension reduction. Compared with the original …

One fundamental statistical question for research areas such as precision medicine and
health disparity is about discovering effect modification of treatment or exposure by …