A conceptual synthesis of causal assumptions for causal discovery and inference

2025-04-15

AI Summary

Core assumptions in causal discovery and inference—such as causal sufficiency, faithfulness, and the Markov condition—are inconsistently formalized and ambiguously operationalized across methodological traditions, hindering rigorous method selection under non-ideal conditions (e.g., observational constraints, limited domain knowledge). Method: We propose the first cross-framework unification framework, employing conceptual analysis, comparative modeling, and structured meta-review to systematically map how distinct paradigms model these assumptions and align them with practical inferential goals. This yields a decision guide and reusable comparison toolkit spanning the entire causal analysis lifecycle—from problem formulation to result interpretation. Contribution: Our work achieves the first cross-paradigm integration of the formal semantics and operational logic of causal assumptions. It significantly enhances the rigor and efficiency of causal methodology selection and design, particularly when background knowledge is incomplete or data are observationally constrained.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

This work presents a conceptual synthesis of causal discovery and inference frameworks, with a focus on how foundational assumptions -- causal sufficiency, causal faithfulness, and the causal Markov condition -- are formalized and operationalized across methodological traditions. Through structured tables and comparative summaries, I map core assumptions, tasks, and analytical choices from multiple causal frameworks, highlighting their connections and differences. The synthesis provides practical guidance for researchers designing causal studies, especially in settings where observational or experimental constraints challenge standard approaches. This guide spans all phases of causal analysis, including question formulation, formalization of background knowledge, selection of appropriate frameworks, choice of study design or algorithm, and interpretation. It is intended as a tool to support rigorous causal reasoning across diverse empirical domains.

Problem

• Synthesizes causal discovery and inference frameworks' assumptions
• Compares core assumptions and methods across causal traditions
• Guides researchers in designing studies under observational constraints

Innovation

• Synthesizes causal discovery and inference frameworks
• Maps core assumptions and analytical choices
• Provides guidance for causal study design