AIMC Topic: Causality

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Combining machine learning and matching techniques to improve causal inference in program evaluation.

Journal of evaluation in clinical practice Jun 29, 2016
RATIONALE, AIMS AND OBJECTIVES: Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balan...
Chronic Disease Discriminant Analysis Random Allocation Male Middle Aged Causality Propensity Score Program Evaluation Humans Machine Learning Algorithms Female Adult
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Transitive closure of subsumption and causal relations in a large ontology of radiological diagnosis.

Journal of biomedical informatics Mar 19, 2016
The Radiology Gamuts Ontology (RGO)-an ontology of diseases, interventions, and imaging findings-was developed to aid in decision support, education, and translational research in diagnostic radiology. The ontology defines a subsumption (is_a) relati...
Biological Ontologies Radiology Translational Research, Biomedical Semantics Decision Support Techniques Causality Radiography Humans
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Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data.

PloS one Oct 5, 2015
It is becoming increasingly necessary to develop computerized methods for identifying the few disease-causing variants from hundreds discovered in each individual patient. This problem is especially relevant for Copy Number Variants (CNVs), which can...
Gene Dosage Genetic Diseases, Inborn Mutation Genetic Association Studies Gene Ontology Humans Genetic Variation Models, Genetic Gene Regulatory Networks Developmental Disabilities Oligonucleotide Array Sequence Analysis Causality Genetic Predisposition to Disease Congenital Abnormalities
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Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

Artificial intelligence in medicine Sep 8, 2015
OBJECTIVES: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model,...
Bayes Theorem Forensic Medicine Information Storage and Retrieval Humans Choice Behavior Neural Networks, Computer Risk Assessment Violence Causality Decision Support Techniques Uncertainty Models, Statistical Computer Simulation Risk Factors
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Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality.

Neural networks : the official journal of the International Neural Network Society Aug 21, 2015
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approache...
Models, Theoretical Causality Classification Entropy Neural Networks, Computer Algorithms Computer Simulation
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Granger causality-based synaptic weights estimation for analyzing neuronal networks.

Journal of computational neuroscience Mar 13, 2015
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC ...
Neural Networks, Computer Nonlinear Dynamics Corpus Striatum Algorithms Causality Synapses Humans Gyrus Cinguli Computer Simulation Linear Models Models, Neurological Electrophysiological Phenomena Action Potentials
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A Bayesian Approach to the G-Formula via Iterative Conditional Regression.

Statistics in medicine Jun 1, 2025
In longitudinal observational studies with time-varying confounders, the generalized computation algorithm formula (g-formula) is a principled tool to estimate the average causal effect of a treatment regimen. However, the standard non-iterative g-fo...
Observational Studies as Topic Humans Models, Statistical Machine Learning Computer Simulation Algorithms Regression Analysis Longitudinal Studies Bayes Theorem Causality
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Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.

Statistics in medicine Mar 15, 2025
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these mo...
Models, Statistical Proportional Hazards Models Likelihood Functions Humans Machine Learning Causality
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A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns.

American journal of epidemiology Jan 8, 2025
When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from o...
Humans Air Pollution Cities Nitrogen Dioxide United States Machine Learning Interrupted Time Series Analysis Causality COVID-19 SARS-CoV-2
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A Modular Service for Causal Inference in the Context of the Gaia-X Ecosystem.

Studies in health technology and informatics Aug 22, 2024
Causal inference seeks to learn the effect of interventions on outcomes. Its potential in the health domain has been dramatically increasing recently, due to advancements in machine learning, as well as in the growing amount of medical data collected...
Machine Learning Algorithms Causality Humans
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