Causal machine learning for healthcare and precision medicine [PDF]
Royal Society Open Science, 2022 Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an ...
Pedro Sanchez +5 more
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Causal Machine Learning for Surgical Interventions [PDF]
Big Data Mining and AnalyticsSurgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spinal fusion or scoliosis correction, accurate estimation of individualized ...
Junior Ben Tamo +8 more
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Causal ML: Python package for causal inference machine learning
SoftwareX, 2023 “Causality” is a complex concept that is based on roots in almost all subject areas and aims to answer the “why” question. Causal inference is one of the important branches of causal analysis, which assumes the existence of relationships between ...
Yang Zhao, Qing Liu
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Inferring the heterogeneous effect of urban land use on building height with causal machine learning
GIScience & Remote SensingMachine learning has become an important approach for land use change modeling. However, conventional machine learning algorithms are limited in their ability to capture causal relationships in land use change, which are important knowledge for planners ...
Yimin Chen +6 more
doaj +4 more sources
How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. [PDF]
PLoS ONE, 2023 We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer.
Henrika Langen, Martin Huber
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Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability [PDF]
Data & Policy, 2023 Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no ...
Patrick Rehill, Nicholas Biddle
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Causal Machine Learning and its use for public policy
Swiss Journal of Economics and Statistics, 2023 In recent years, microeconometrics experienced the ‘credibility revolution’, culminating in the 2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens.
Michael Lechner
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Real-World Data and Causal Machine Learning to Enhance Drug Development. [PDF]
Ther Innov Regul SciThe current paradigm of clinical drug development, which predominantly relies on traditional randomized controlled trials (RCTs), is increasingly challenged by inefficiencies, escalating costs, and limited generalizability.
Anagnostopoulos C +3 more
europepmc +2 more sources
Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning
Ocean-Land-Atmosphere Research, 2023 It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness ...
X. San Liang, Dake Chen, Renhe Zhang
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Causal machine learning for supply chain risk prediction and intervention planning [PDF]
International Journal of Production ResearchThe ultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to ...
George Baryannis +2 more
exaly +2 more sources