Summary of Nondeterministic Causal Models, by Sander Beckers
Nondeterministic Causal Models
by Sander Beckers
First submitted to arxiv on: 22 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach generalizes acyclic deterministic structural causal models to nondeterministic cases, offering an improved semantics for counterfactuals. The standard deterministic semantics assumes unique assignments of values to parent and child variables, as well as a unique actual world specifying a counterfactual world for each intervention. However, these assumptions are unrealistic, so the proposal drops them by allowing multi-valued functions in structural equations and adjusting the semantics to preserve solutions in any counterfactual world. A sound and complete axiomatization of the resulting logic is provided, along with comparisons to standard approaches and recent proposals. The approach is further extended to probabilistic cases, enabling identification of counterfactuals in Causal Bayesian Networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how things would have turned out if something had been different is introduced. Currently, our understanding of “what if” scenarios assumes that everything else stayed the same, but this approach relaxes those assumptions. It’s like allowing for multiple possible outcomes instead of just one. The idea is to make it more realistic by not assuming that everything else stays the same when we ask what would have happened if something had been different. |
Keywords
» Artificial intelligence » Semantics
