OBJECTIVE To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contracept...

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyc...

This paper argues that the current way in which the undergraduate introductory econometrics course is taught is neither inline with current empirical practice nor very intuitive. It proposes a shift in focus of the course on causal inference using the Roy-Rubin Causal Model (RRCM). A second theme of the paper is the suggestion to use random regressors from the start to improve the ability of st...

Gastroesophageal reflux disease (GERD), or its variation known as laryngopharyngeal reflux (LPR), has been recognized as a potential cause of several laryngeal disorders. Patients with laryngeal cancer have lifestyle risk factors, especially tobacco and alcohol consumption, that play an etiological role in the development of their cancer but also places them at risk for reflux. The question the...

The emergence of big data analytics enables real time news analysis. Such analysis offers the possibility to instantly extract the sentiment conveyed by any newly published, textual information source. This paper investigates the existence of a causal relationship between news sentiment and stock prices. As such, we apply news sentiment analysis for unstructured, textual data to extract sentime...

The order in which people receive information has a substantial effect on subsequent judgment and inference. Our focus is on the order of covariation evidence in causal learning. The first experiment shows that the initial presentation of evidence suggesting a generative causal relationship (the joint presence or joint absence of cause and effect) leads to higher judged causal strength than doe...

Leveraging the relationship between Wiener filtering and the coherence function, a version of coherence is defined that captures the causal relationship between WSS processes. This causal coherence is interpreted in a modeling context and used to demonstrate what a frequency dependent measure for causality both can and can’t represent. To understand how well frequency dependent coherence spectr...