Towards Robust Causal Effect Identification Beyond Markov Equivalence

2025-06-18

AI Summary

Causal effect identification typically assumes a uniquely determined causal graph; however, in practice, untestable assumptions—such as faithfulness—often fail, yielding only a set of candidate Markov equivalence classes (MECs) rather than a single graph. Method: We propose the first sufficient identifiability criterion that leverages both a given set of candidate MECs and domain-specific background knowledge, relaxing the conventional requirement of a unique causal graph or a single MEC. Our approach integrates do-calculus, MEC-based analysis, and formal modeling of prior knowledge to derive verifiable identification conditions. Contribution/Results: Experiments demonstrate that our framework substantially improves both the identifiability and robustness of causal effects under challenging settings—including non-faithful models—thereby extending the applicability boundary of causal inference under realistic, incomplete information.

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: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold.

Problem

• Identifying causal effects without full graph specification
• Using background knowledge for Markov equivalence classes
• Addressing challenges when causal discovery assumptions fail

Innovation

• Uses Markov equivalence classes for identification
• Incorporates background knowledge for robustness
• Addresses untestable assumptions in causal discovery