Results 41 to 50 of about 1,076,580 (347)

Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

Journal of Causal Inference, 2014
The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument.
de Luna Xavier, Johansson Per
doaj   +1 more source

Evaluating integrated maternity care policies with causal inference methods and routine-collected observational data [PDF]

, 2023
Anouk Klootwijk, Zoë T. M. Scheefhals, Jeroen N. Struijs   +2 more
openalex   +1 more source

Characterizing Parental Concerns About Lasting Impacts of Treatment in Children With B‐Acute Lymphoblastic Leukemia

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background B‐acute lymphoblastic leukemia (B‐ALL) is the most common pediatric cancer, and while most children in high‐resource settings are cured, therapy carries risks for long‐term toxicities. Understanding parents’ concerns about these late effects is essential to guide anticipatory support and inform evolving therapeutic approaches ...
Kellee N. Parker, Sarah W. Alexander, Lisa M. Jacola, Kimberly A. Buff, Kyobin Hwang, Elham Hashemi, Gregory Stoddard, Lindsay A. Jibb   +7 more
wiley   +1 more source

Prospective and retrospective causal inferences based on the potential outcome framework

Journal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen, Wei Shaojie   +4 more
doaj   +1 more source

CauseKG: A Framework Enhancing Causal Inference With Implicit Knowledge Deduced From Knowledge Graphs

IEEE Access
Causal inference is a critical technique for inferring causal relationships from data and distinguishing causation from correlation. Causal inference frameworks rely on structured data, typically represented in flat tables or relational models.
Hao Huang, Maria-Esther Vidal
doaj   +1 more source

Causality, a Trialogue

Journal of Causal Inference, 2014
A philosopher, a medical doctor, and a statistician talk about causality. They discuss the relationships between causality, chance, and statistics, resorting to examples from medicine to develop their arguments.
Chambaz Antoine, Drouet Isabelle, Thalabard Jean-Christophe   +2 more
doaj   +1 more source

Causal Inference Meets Deep Learning: A Comprehensive Survey

Research
Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness.
Licheng Jiao, Yuhan Wang, Xu Liu, Lingling Li, Fang Liu, Wenping Ma, Yuwei Guo, Puhua Chen, Shuyuan Yang, B. Hou   +9 more
semanticscholar   +1 more source

A Comparative Study of Cerebral Oxygenation During Exercise in Hemodialysis and Peritoneal Dialysis Patients

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Introduction Cognitive impairment and exercise intolerance are common in dialysis patients. Cerebral perfusion and oxygenation play a major role in both cognitive function and exercise execution; HD session per se aggravates cerebral ischemia in this population. This study aimed to compare cerebral oxygenation and perfusion at rest and in mild
Marieta P. Theodorakopoulou, Konstantina Dipla, Danai Faitatzidou, Fotini Iatridi, Areti Georgiou, Artemios Karagiannidis, Erasmia Sampani, Chrysostomos Dimitriadis, Aggelos Koutlas, Andreas Zafeiridis, Pantelis Sarafidis   +10 more
wiley   +1 more source

Controlling human causal inference through in silico task design

Cell Reports
Summary: Learning causal relationships is crucial for survival. The human brain’s functional flexibility allows for effective causal inference, underlying various learning processes.
Jee Hang Lee, Su Yeon Heo, Sang Wan Lee
doaj   +1 more source

Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants

Epidemiology, 2016
Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk ...
S. Burgess, J. Bowden, T. Fall, E. Ingelsson, S. Thompson   +4 more
semanticscholar   +1 more source