Reducing bias from missing confounders is a challenging problem in the analysis of observational data. Information about missing variables is sometimes available from external validation data, such as surveys or secondary samples drawn from the same source population. In principle, the validation data permits us to recover information about the missing data, but the difficulty is in eliciting a...

Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task many real world applications, such as finance, retail, healthcare, etc. Real series, i.e., large-scale irregular or sparse and intermittent raise significant challenges to existing work attempting estimate effects. Specifically, the existence of hidden confounders can lead biased ...

Confounding is present in most observational studies. Yet by its nature, confounding is not generally present in the data. In order to use statistical associations as evidence for causal connections, one must try to take into account the influence of confounding. This paper reviews the role of confounding in the epic debate between Cornfield and Fisher on the statistical association between smo...

Numerous papers (1–6), including a recent metaanalysis (7), have contributed to the growing literature investigating whether breastfeeding can enhance child cognitive development, as assessed by IQ. Virtually all the Ž ndings to date concur in showing an association between better performance on IQ tests in children who were breastfed over those who were bottlefed. This intellectual advantage h...

We read the article entitled Serum uric acid as a prognostic marker in the setting of advanced vascular disease: a prospective study in the elderly by Stolfo, et al. with great interest. The authors evaluated the association of serum uric acid (SUA) levels with adverse cardiovascular events and deaths in an elderly population affected by advanced atherosclerosis. They founded meaningful associa...

Motivated by online recommendation and advertising systems, we consider a causal model for stochastic contextual bandits with a latent low-dimensional confounder. In our model, there are L observed contexts and K arms of the bandit. The observed context influences the reward obtained through a latent confounder variable with cardinality m (m ⌧ L, K). The arm choice and the latent confounder cau...

Artificial data with known covariance structure was used to directly test confounding hypothesis using Bayesian confirmatory factor analysis with small variance informative priors. The priors were derived from two extreme scenarios of no versus maximum confounding via exposure variables. Large (N=5000) and small (N=100) sample analyses were performed for both continuous and binary variable mode...