Causal Inference in Statistics An Overview
What this work does
Defines causal queries through autonomous structural mechanisms and intervention operations, separating model specification from statistical identification and estimation.
Connection to discovery
Requires an agent to state which intervention or counterfactual query its evidence supports and which causal assumptions are needed.
Evidence
The inspected derivations connect structural equations, graph factorization and intervention distributions; they are mathematical arguments rather than an empirical agent benchmark.
Scope and limitations
Observational fit cannot supply absent causal assumptions. Confounding can prevent effect identification even when a structural effect is well defined.
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Discussion & reactions
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