Causal identification with $Y_0$

2025-08-05

AI Summary

This work addresses the fundamental identifiability problem in causal inference—determining whether a target causal quantity is identifiable from available data. We propose a unified qualitative causal identification framework supporting three classes of queries: interventional, counterfactual, and cross-domain transportability. Methodologically, we model latent-variable causal structures using acyclic directed mixed graphs (ADMGs), integrate state-of-the-art identification algorithms, and design a domain-specific language (DSL) for declarative specification and symbolic reasoning over causal expressions. Our contributions are threefold: (1) the first unification of diverse causal query types under a single identification paradigm; (2) automated nonparametric identifiability assessment under observational, experimental, or hybrid data regimes; and (3) an open-source Python package (installable via pip) that outputs estimable closed-form symbolic expressions, thereby enhancing rigor, interpretability, and reproducibility in causal modeling.

Technology Category

Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningKnowledge Representation and Reasoning: Action, Change, and Causality

Application Category

Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Implements causal identification algorithms for diverse data types
• Determines if causal relationships can be estimated from data
• Transforms causal queries into symbolic estimands for estimation

Innovation

• Implements causal identification algorithms
• Provides domain-specific language for queries
• Supports causal graphical models with confounders