Partial Identification under Causal Orders by Linear Programming
Quick summary
arXiv:2608.24427v1 Announce Type: new Abstract: Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph. Motivated by settings with incomplete domain knowledge, we challenge this requirement by leveraging structural assumptions that are inherently implied by the query itself. We show that any counterfactual inquiry induces a, mostly partial, topological ordering over relevant variables, which, in turn, enables an explicit query parametrisation reducing the identification task to a linear program. This allows bounding arbitrary counte
Key takeaways
- arXiv:2608.24427v1 Announce Type: new Abstract: Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph.
- Motivated by settings with incomplete domain knowledge, we challenge this requirement by leveraging structural assumptions that are inherently implied by the query itself.
- We show that any counterfactual inquiry induces a, mostly partial, topological ordering over relevant variables, which, in turn, enables an explicit query parametrisation reducing the identification task to a linear program.
Why it matters
The importance of “Partial Identification under Causal Orders by Linear Programming” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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