Mediation Analysis for Probabilities of Causation
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910752377405440 |
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| author | Kawakami, Yuta Tian, Jin |
| author_facet | Kawakami, Yuta Tian, Jin |
| contents | Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mediation Analysis for Probabilities of Causation Kawakami, Yuta Tian, Jin Artificial Intelligence Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset. |
| title | Mediation Analysis for Probabilities of Causation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.14491 |