Interpretational Confounding or Confounded Interpretations of Causal Indicators?
نویسندگان
چکیده
منابع مشابه
Prognostic relevance of coronary collateral function: confounded or causal relationship?
OBJECTIVE To expand the limited information on the prognostic impact of quantitatively obtained collateral function in patients with coronary artery disease (CAD) and to estimate causality of such a relation. DESIGN Prospective cohort study with long-term observation of clinical outcome. SETTING University Hospital. PATIENTS One thousand one hundred and eighty-one patients with chronic st...
متن کاملCausal Interpretations of Probability
scientific theories or hypotheses. Since the range of alternatives is not known in these cases, it seems implausible to construct a collective and relatedly the measure remains undetermined. If one requires probabilities to be predictive, the range of hypotheses to which probabilities should be ascribed is thus rather restricted. 44 We are therefore in the position to assess the plausibility of...
متن کاملConfounding Equivalence in Causal Inference
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We...
متن کاملThe association of body mass index with health outcomes: causal, inconsistent, or confounded?
According to the definition of confounding in a causal diagram, the association of body mass index (weight (kg)/height (m)(2)) with health-related outcomes is almost always noncausal, attributable to confounding by weight and perhaps height. The same conclusion holds for any other deterministic derivation from weight and height. No causal knowledge is gained by estimating a nonexistent effect o...
متن کاملConfounded: Causal Inference and the Requirement of Independence
One of the most important requirements for accurate causal inference is that there be no confounds; the cause being evaluated must occur independently of all other causes. When this requirement is not met, causal inferences are likely to be incorrect. The current study asks participants to judge how informative various situations are with respect to drawing causal inferences. Contrary to normat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Measurement: Interdisciplinary Research and Perspectives
سال: 2014
ISSN: 1536-6367,1536-6359
DOI: 10.1080/15366367.2014.968503