Causal identification with $Y_0$
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909723171749888 |
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| author | Hoyt, Charles Tapley Bakker, Craig Callahan, Richard J. Cottam, Joseph George, August Gyori, Benjamin M. Hummel, Haley M. Merrill, Nathaniel Taheri, Sara Mohammad Navada, Pruthvi Prakash Parent, Marc-Antoine Rupe, Adam Vitek, Olga Zucker, Jeremy |
| author_facet | Hoyt, Charles Tapley Bakker, Craig Callahan, Richard J. Cottam, Joseph George, August Gyori, Benjamin M. Hummel, Haley M. Merrill, Nathaniel Taheri, Sara Mohammad Navada, Pruthvi Prakash Parent, Marc-Antoine Rupe, Adam Vitek, Olga Zucker, Jeremy |
| contents | We present the $Y_0$ Python package, which implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data from (randomized) controlled trials, observational studies, or mixtures thereof. $Y_0$ focuses on the qualitative investigation of causation, helping researchers determine whether a causal relationship can be estimated from available data before attempting to estimate how strong that relationship is. Furthermore, $Y_0$ provides guidance on how to transform the causal query into a symbolic estimand that can be non-parametrically estimated from the available data. $Y_0$ provides a domain-specific language for representing causal queries and estimands as symbolic probabilistic expressions, tools for representing causal graphical models with unobserved confounders, such as acyclic directed mixed graphs (ADMGs), and implementations of numerous identification algorithms from the recent causal inference literature. The $Y_0$ source code can be found under the MIT License at https://github.com/y0-causal-inference/y0 and it can be installed with pip install y0. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal identification with $Y_0$ Hoyt, Charles Tapley Bakker, Craig Callahan, Richard J. Cottam, Joseph George, August Gyori, Benjamin M. Hummel, Haley M. Merrill, Nathaniel Taheri, Sara Mohammad Navada, Pruthvi Prakash Parent, Marc-Antoine Rupe, Adam Vitek, Olga Zucker, Jeremy Artificial Intelligence We present the $Y_0$ Python package, which implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data from (randomized) controlled trials, observational studies, or mixtures thereof. $Y_0$ focuses on the qualitative investigation of causation, helping researchers determine whether a causal relationship can be estimated from available data before attempting to estimate how strong that relationship is. Furthermore, $Y_0$ provides guidance on how to transform the causal query into a symbolic estimand that can be non-parametrically estimated from the available data. $Y_0$ provides a domain-specific language for representing causal queries and estimands as symbolic probabilistic expressions, tools for representing causal graphical models with unobserved confounders, such as acyclic directed mixed graphs (ADMGs), and implementations of numerous identification algorithms from the recent causal inference literature. The $Y_0$ source code can be found under the MIT License at https://github.com/y0-causal-inference/y0 and it can be installed with pip install y0. |
| title | Causal identification with $Y_0$ |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.03167 |