Randomization and conditional independence of treatment and observables imply unconfoundedness
Is it true that Randomization and conditional independence of treatment and observables imply unconfoundedness?
The relationship between randomization, conditional independence, and unconfoundedness is a foundational definition within causal inference methodology, making this claim common knowledge that requires no citation.
Verdict: COMMON KNOWLEDGE · 0 sources for, 0 against · Last checked
The claim states a fundamental methodological definition in causal inference: that randomization combined with conditional independence of observables yields unconfoundedness (or strong ignorability). This is standard textbook definition and foundational statistical theory rather than an empirical hypothesis subject to scientific debate. Following the instructions for STEP 0, trivially true definitions or foundational disciplinary concepts are classified as COMMON_KNOWLEDGE, requiring no citations.
What does the evidence say?
The claim states a fundamental methodological definition in causal inference: that randomization combined with conditional independence of observables yields unconfoundedness (or strong ignorability). This is standard textbook definition and foundational statistical theory rather than an empirical hypothesis subject to scientific debate. Following the instructions for STEP 0, trivially true definitions or foundational disciplinary concepts are classified as COMMON_KNOWLEDGE, requiring no citations.
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