Results 31 to 40 of about 8,838,426 (323)
A Review of Causality for Learning Algorithms in Medical Image Analysis
, 2022 Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area.
Vlontzos, Athanasios +2 more
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Long Story Short: Omitted Variable Bias in Causal Machine Learning [PDF]
Social Science Research Network, 2021 We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts.
V. Chernozhukov +4 more
semanticscholar +1 more source
Multiscale causal structure learning [PDF]
, 2023 Causal structure learning methods are vital for unveiling causal relationships embedded into observed data. However, the state of the art suffers a major limitation: it assumes that causal interactions occur only at the frequency at which data is ...
Sergio Barbarossa +2 more
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Towards assessing agricultural land suitability with causal machine learning [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change.
Georgios Giannarakis +3 more
semanticscholar +1 more source
Causality, machine learning and human insight
Analytica Chimica Acta, 2023 Modern instruments generate BIG DATA that require information extraction before they can be used. A hybrid modelling framework for that is presented and illustrated. Its purpose is to convert meaningless data to meaningful information and to contribute to a theoretical, practical, and democratic basis for tomorrow's handling of BIG DATA in science and ...
openaire +3 more sources
Recent Developments in Causal Inference and Machine Learning
Annual Review of Sociology, 2022 This paper provides an updated review of the latest advances in causal inference in sociology and other disciplines. We focus on four topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We show how machine learning, as an estimation strategy, can be
Jennie E. Brand, Xiang Zhou, Yu Xie
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Individual differences in causal learning and decision making [PDF]
, 2005 This is an accepted author manuscript of an article subsequently published by Elsevier. The final published version can be found here: http://dx.doi.org/10.1016/j.actpsy.2005.04.003In judgment and decision making tasks, people tend to neglect the overall
Osman, M, Shanks, David R
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Personalizing Sustainable Agriculture with Causal Machine Learning [PDF]
arXiv.org, 2022 To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the “sustainable intensification” of agriculture, transforming it from carbon emitter to carbon sink is a priority, and ...
Georgios Giannarakis +3 more
semanticscholar +1 more source
Journal of Geographical Systems, 2023 The development of the “causal” forest by Wager and Athey (J Am Stat Assoc 113(523): 1228–1242, 2018 ) represents a significant advance in the area of explanatory/causal machine learning.
Kevin Credit, Matthew Lehnert
semanticscholar +1 more source
Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning [PDF]
Social Science Research Network, 2021 Active labor market programs are important instruments used by European employment agencies to help the unemployed find work. Investigating large administrative data on German long-term unemployed persons, we analyze the effectiveness of three job search
D. Goller +3 more
semanticscholar +1 more source