Staged Event Trees for Transparent Treatment Effect Estimation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909816718360576 |
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| author | Varando, Gherardo Leonelli, Manuele Cerdà-Bautista, Jordi Sitokonstantinou, Vasileios Camps-Valls, Gustau |
| author_facet | Varando, Gherardo Leonelli, Manuele Cerdà-Bautista, Jordi Sitokonstantinou, Vasileios Camps-Valls, Gustau |
| contents | Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical settings and policy-making. Beyond the gold-standard estimators from randomized trials, numerous methods have been proposed to estimate treatment effects using observational data. In this paper, we provide a novel characterization of widely used causal inference techniques within the framework of staged event trees, demonstrating their capacity to enhance treatment effect estimation. These models offer a distinct advantage due to their interpretability, making them particularly valuable for practical applications. We implement classical estimators within the framework of staged event trees and illustrate their capabilities through both simulation studies and real-world applications. Furthermore, we showcase how staged event trees explicitly and visually describe when standard causal assumptions, such as positivity, hold, further enhancing their practical utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Staged Event Trees for Transparent Treatment Effect Estimation Varando, Gherardo Leonelli, Manuele Cerdà-Bautista, Jordi Sitokonstantinou, Vasileios Camps-Valls, Gustau Methodology Machine Learning Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical settings and policy-making. Beyond the gold-standard estimators from randomized trials, numerous methods have been proposed to estimate treatment effects using observational data. In this paper, we provide a novel characterization of widely used causal inference techniques within the framework of staged event trees, demonstrating their capacity to enhance treatment effect estimation. These models offer a distinct advantage due to their interpretability, making them particularly valuable for practical applications. We implement classical estimators within the framework of staged event trees and illustrate their capabilities through both simulation studies and real-world applications. Furthermore, we showcase how staged event trees explicitly and visually describe when standard causal assumptions, such as positivity, hold, further enhancing their practical utility. |
| title | Staged Event Trees for Transparent Treatment Effect Estimation |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2509.26265 |