Results 51 to 60 of about 158,058 (264)
Practically effective adjustment variable selection in causal inference
Journal of Physics: ComplexityIn the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not always possible to uniquely determine sets of such variables.
Atsushi Noda, Takashi Isozaki
doaj +1 more source
An [Imperfect] Case for Dyadic Research in Pediatric Psychosocial Oncology
Pediatric Blood &Cancer, EarlyView.
Stephanie M. Nanos +2 more
wiley +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Establishing a comprehensive apheresis medicine program in a resource‐constrained setting presents significant structural, financial, and logistical challenges. Despite the growing clinical importance of apheresis services globally, published experience from sub‐Saharan Africa remains sparse.
Folasade Adelekan‐Popoola +4 more
wiley +1 more source
Genetics Selection Evolution, 2022 Background The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term “effect” carries a causal meaning that can be confounded because of potential genetic associations ...
Mónica Mora +4 more
doaj +1 more source
Individual Causal Inference with Structural Causal Model
CoRRIndividual causal inference (ICI) uses causal inference methods to understand and predict the effects of interventions on individuals, considering their specific characteristics / facts. It aims to estimate individual causal effect (ICE), which varies across individuals.
openaire +2 more sources
A Distinction between Causal Effects in Structural and Rubin Causal Models [PDF]
SSRN Electronic Journal, 2015 Structural Causal Models define causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defines causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different definitions, notationally similar causal effects make distinct claims about the results of interventions to the ...
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source
Decision making, symmetry and structure: Justifying causal interventions
Journal of Causal InferenceWe can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified.
Johnston David O. +2 more
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Interdisciplinary Research on Healthy Aging: Introduction
Demographic Research, 2018 Background: This is an introduction to a Special Collection of Demographic Research on Interdisciplinary Research on Healthy Aging. The collection is an outcome of an international conference in China on biodemography and multistate modeling in healthy ...
Frans Willekens +2 more
doaj +1 more source