AIMC Topic: Causality

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Decomposing predictability to identify dominant causal drivers in complex ecosystems.

Proceedings of the National Academy of Sciences of the United States of America Oct 10, 2022
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods ...
Neural Networks, Computer Lakes Causality Ecosystem Machine Learning
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Surgery duration: Optimized prediction and causality analysis.

PloS one Aug 29, 2022
Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients' waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose ...
Causality Machine Learning Humans Algorithms
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A structural characterization of shortcut features for prediction.

European journal of epidemiology Jul 6, 2022
With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are trained in one setting but meant to be deployed in several others. One recently identifi...
Causality Machine Learning Humans
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Nonparametric estimation of the causal effect of a stochastic threshold-based intervention.

Biometrics May 26, 2022
Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. In view of this goal, we consider a covariate-adjusted threshold-based interventional estimand, which hap...
Bias Models, Statistical Probability Machine Learning Humans Causality
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Chemical property prediction under experimental biases.

Scientific reports May 17, 2022
Predicting the chemical properties of compounds is crucial in discovering novel materials and drugs with specific desired characteristics. Recent significant advances in machine learning technologies have enabled automatic predictive modeling from pa...
Causality Bias Machine Learning Neural Networks, Computer
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Mining human preference via self-correction causal structure learning.

Scientific reports Mar 23, 2022
Spurred by causal structure learning (CSL) ability to reveal the cause-effect connection, significant research efforts have been made to enhance the scalability of CSL algorithms in various artificial intelligence applications. However, less effort h...
Algorithms Causality Artificial Intelligence Humans
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State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 21, 2022
The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into tw...
Models, Statistical Bias Humans Computer Simulation Causality
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Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 21, 2022
"No-shows", defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slow its sprea...
COVID-19 Humans Israel Causality Pandemics Appointments and Schedules
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Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Statistics in medicine Feb 16, 2022
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe...
Regression Analysis Likelihood Functions Causality Models, Statistical Computer Simulation Humans Research Design Machine Learning
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Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding.

Psychometrika Oct 15, 2021
Recently, machine learning (ML) methods have been used in causal inference to estimate treatment effects in order to reduce concerns for model mis-specification. However, many ML methods require that all confounders are measured to consistently estim...
Causality Humans Machine Learning Confounding Factors, Epidemiologic Child, Preschool Psychometrics Longitudinal Studies Bias
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