Results 61 to 70 of about 47,050 (138)

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

IEEE Open Journal of Signal Processing
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion
Daniel Waxman, Kurt Butler, Petar M. Djuric   +2 more
doaj   +1 more source

Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning

Materials & Design
This work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED ...
André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao   +6 more
doaj   +1 more source

Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Timely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction.
Fumin Wang, Jiale Li, Dailiang Peng, Qiuxiang Yi, Xiaoyang Zhang, Jueyi Zheng, Siting Chen   +6 more
doaj   +1 more source

Causal Machine Learning for Surgical Interventions

Surgical 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 treatment effects (ITEs) remains limited due to the reliance on traditional statistical methods that ...
Tamo, J. Ben, Chouhan, Nishant S., Nnamdi, Micky C., Yuan, Yining, Chivilkar, Shreya S., Shi, Wenqi, Hwang, Steven W., Brenn, B. Randall, Wang, May D.   +8 more
openaire   +2 more sources

Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound

Environmental Science and Ecotechnology
Ground-level ozone concentrations rebounded significantly across China in 2022, challenging air quality management and public health. Identifying the drivers of this rebound is crucial for designing effective mitigation strategies. Commonly used methods,
Lin Wang, Baihua Chen, Jingyi Ouyang, Yanshu Mu, Ling Zhen, Lin Yang, Wei Xu, Lina Tang   +7 more
doaj   +1 more source

Causal Economic Machine Learning (CEML)

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal ...
openaire   +1 more source

Causal machine learning for sustainable agroecosystems

In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learning (ML), with its capacity to learn from data, is leveraged in sustainable agriculture for applications
Sitokonstantinou, Vasileios, Porras, Emiliano Díaz Salas, Bautista, Jordi Cerdà, Piles, Maria, Athanasiadis, Ioannis, Kerner, Hannah, Martini, Giulia, Sweet, Lily-belle, Tsoumas, Ilias, Zscheischler, Jakob, Camps-Valls, Gustau   +10 more
openaire   +2 more sources

Step-by-step causal analysis of EHRs to ground decision-making.

PLOS Digital Health
Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover ...
Matthieu Doutreligne, Tristan Struja, Judith Abecassis, Claire Morgand, Leo Anthony Celi, Gaël Varoquaux   +5 more
doaj   +1 more source

Institutions and the resource curse: New insights from causal machine learning. [PDF]

PLoS One, 2023
Hodler R, Lechner M, Raschky PA.
europepmc   +1 more source

Targeting resources efficiently and justifiably by combining causal machine learning and theory. [PDF]

Front Artif Intell, 2022
Gur Ali O.
europepmc   +1 more source