Applied Causal Inference Powered by ML and AI
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910353933205504 |
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| author | Chernozhukov, Victor Hansen, Christian Kallus, Nathan Spindler, Martin Syrgkanis, Vasilis |
| author_facet | Chernozhukov, Victor Hansen, Christian Kallus, Nathan Spindler, Martin Syrgkanis, Vasilis |
| contents | An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Applied Causal Inference Powered by ML and AI Chernozhukov, Victor Hansen, Christian Kallus, Nathan Spindler, Martin Syrgkanis, Vasilis Econometrics Machine Learning Methodology An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools. |
| title | Applied Causal Inference Powered by ML and AI |
| topic | Econometrics Machine Learning Methodology |
| url | https://arxiv.org/abs/2403.02467 |