AIMC Topic: Causality

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Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation.

International journal of neural systems Jun 17, 2023
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effec...
Machine Learning Neural Networks, Computer Causality
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Estimation of separable direct and indirect effects in a continuous-time illness-death model.

Lifetime data analysis Jun 4, 2023
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corres...
Computer Simulation Causality Artificial Intelligence Biometry Mediation Analysis Humans Survival Analysis Models, Statistical
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Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.

Molecular systems biology May 26, 2023
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue R...
Risk Assessment Causality Machine Learning Rare Diseases Humans
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Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.

BMC medical research methodology May 22, 2023
To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders an...
Machine Learning Causality Humans Bias Confounding Factors, Epidemiologic Observational Studies as Topic
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Well-being impact assessment of artificial intelligence - A search for causality and proposal for an open platform for well-being impact assessment of AI systems.

Evaluation and program planning Apr 26, 2023
In recent years, the well-being impact assessment approach has been applied in the area of Artificial Intelligence (AI). Existing well-being frameworks and tools provide a relevant starting point. Taking into account its multidimensional nature, well...
Causality Program Evaluation Artificial Intelligence Technology Humans
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Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation.

Journal of biomedical informatics Mar 20, 2023
A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. In healthcare, the gold standard for causal effect measurements is randomi...
Algorithms Machine Learning Randomized Controlled Trials as Topic Humans Causality
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An introduction to causal inference for pharmacometricians.

CPT: pharmacometrics & systems pharmacology Dec 8, 2022
As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic f...
Artificial Intelligence Humans Data Interpretation, Statistical Causality Confounding Factors, Epidemiologic Biometry
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Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.

Journal of biomedical informatics Nov 28, 2022
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of ...
Causality Humans Randomized Controlled Trials as Topic Benchmarking Machine Learning
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A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

International journal of environmental research and public health Nov 12, 2022
Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodolo...
Causality Bias Databases, Factual Machine Learning Humans Research Design
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Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.

PLoS computational biology Oct 20, 2022
Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a meth...
Causality Deep Learning Mendelian Randomization Analysis Research Design Genomics
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