We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication...

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naı̈ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...

In defining selection bias, we have considered only the parallel universe of the treated group or the untreated group rather than including the parallel universe of the untreated group or the treated group. This makes causal inference theories unbalanced because they were developed on one side...

Randomised controlled or clinical trials (RCTs) are generally viewed as the most reliable method to draw causal inference as to the effects of a treatment, as they should guarantee that the individuals being compared differ only in terms of their exposure to the treatment of interest. This...

We investigate identification of causal parameters in case-control and related studies. The odds ratio in the sample is our main estimand of interest and we articulate its relationship with causal parameters under various scenarios. It turns out that the odds ratio is generally a sharp upper...