# Causal identification with Y0

arXiv: https://arxiv.org/abs/2508.03167 · HTML: https://mlsanity.com/paper/2508.03167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [Nested Counterfactual Identification from Arbitrary Surrogate Experiments](https://mlsanity.com/paper/2107.03190/) (2021)
- [Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders](https://mlsanity.com/paper/1807.03024/) (2018)
- [Identifying Causal Effects with the R Package causaleffect](https://mlsanity.com/paper/1806.07161/) (2018)
- [Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias](https://mlsanity.com/paper/1901.00433/) (2019)
- [Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach](https://mlsanity.com/paper/1902.01073/) (2019)
- [Causal Inference Using Tractable Circuits](https://mlsanity.com/paper/2202.02891/) (2022)
- [Hierarchical Causal Models](https://mlsanity.com/paper/2401.05330/) (2024)
- [Ananke: A Python Package For Causal Inference Using Graphical Models](https://mlsanity.com/paper/2301.11477/) (2023)
- [Simplifying Probabilistic Expressions in Causal Inference](https://mlsanity.com/paper/1806.07082/) (2018)
- [Automated Efficient Estimation using Monte Carlo Efficient Influence Functions](https://mlsanity.com/paper/2403.00158/) (2024)
- [Algorithmic Causal Effect Identification with causaleffect](https://mlsanity.com/paper/2107.04632/) (2021)
- [Identifying Counterfactual Queries with the R Package cfid](https://mlsanity.com/paper/2210.14745/) (2022)
- [Surrogate Outcomes and Transportability](https://mlsanity.com/paper/1806.07172/) (2018)

## Abstract (arXiv)

We present the Y0 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. Y0 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, Y0 provides guidance on how to transform the causal query into a symbolic estimand that can be non-parametrically estimated from the available data. Y0 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 Y0 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.