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Causal inference [1] is primarily reliant on a set of core assumptions. While these assumptions enable identification of causal effects, their validity can vary significantly across domains and data settings. This chapter expands on the assumptions already introduced, focusing on their implications, limitations, and diagnostic strategies.

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Assumptions and Real-World Challenges in Causal Inference

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  • 发表日期：

    2025年

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  • Durai Rajamanickam

摘要

Causal inference [1] is primarily reliant on a set of core assumptions. While these assumptions enable identification of causal effects, their validity can vary significantly across domains and data settings. This chapter expands on the assumptions already introduced, focusing on their implications, limitations, and diagnostic strategies.