Causal identification with $Y_0$

• URL: http://arxiv.org/abs/2508.03167v1
• Date: Tue, 05 Aug 2025 07:13:33 GMT
• Title: Causal identification with $Y_0$
• Authors: Charles Tapley Hoyt, Craig Bakker, Richard J. Callahan, Joseph Cottam, August George, Benjamin M. Gyori, Haley M. Hummel, Nathaniel Merrill, Sara Mohammad Taheri, Pruthvi Prakash Navada, Marc-Antoine Parent, Adam Rupe, Olga Vitek, Jeremy Zucker,
• Abstract summary: $Y_$ implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data.<n>$Y_$ provides a domain-specific language for representing causal queries and estimands.<n>$Y_$ can be installed with pip install y0.
• Score: 3.5245733269245276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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