Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control str...

Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relati...

In a recent pair of articles (Redei 1996, 1997), Miklos Redei has taken enormous strides toward characterizing the conditions under which relativistic quantum field theory is a safe setting for the deployment of causal talk. Here, we challenge the adequacy of the accounts of causal dependence and screening off on which rests the relevance of Redei’s theorems to the question of causal good behav...

Data-driven techniques based on multivariate statistics (such as principal component analysis (PCA) and partial least squares (PLS)) have been applied widely to chemical processes and their effectiveness for fault detection is well recognized. There is an inherent limitation on the ability for purely data-driven techniques to identify and diagnose faults, especially when the abnormal situations...

We study m-transportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m ≥ 1 source environments to estimate a causal effect in a given target environment. We provide a novel characterization of mtransportability that directly exploits the completeness of docalculus to obtain the necessary and sufficient ...

Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based framework for drawing causal inference from observational data. ZaliQL supports the state-of-the...

In constructing probabilistic networks from human judgments, we use causal relationships to convey useful patterns of dependencies. The converse task, that of inferring causal relationships from patterns of dependencies, is far less understood. Th’ 1s paper establishes conditions under which the directionality of some interactions can be determined from non-temporal probabilistic information an...

What factors determine whether a young child will learn a new word? Although there are surely numerous contributors, the current investigation highlights the role of causal information. Three-year-old children (N = 36) were taught 6 new words for unfamiliar objects or animals. Items were described in terms of their causal or noncausal properties. When tested only minutes after training, no sign...

Current theories of cognitive developmental debate the extent to which children’s cognition depends on a priori conceptual knowledge. The current set of experiments explores this debate in the domain of causal reasoning: To what degree do preschoolers rely on knowledge about causal relations vs. attention to temporal structure? In three experiments, causal structure and temporal order were eith...