Summary of Constrained Identifiability Of Causal Effects, by Yizuo Chen et al.
Constrained Identifiability of Causal Effects
by Yizuo Chen, Adnan Darwiche
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Methodology (stat.ME)
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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 This paper addresses the challenge of identifying causal effects in complex systems with constraints, such as logical restrictions, in addition to the traditional causal graph. The authors formalize the concept of constrained identifiability, which considers these additional constraints when assessing whether a causal effect can be determined from data. They propose an Arithmetic Circuits (AC) framework for testing constrained identifiability, building upon existing algorithms like do-calculus. This approach is shown to be at least as complete as previous methods and is demonstrated through examples to be effective in identifying previously unidentifiable causal effects under specific constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out what causes something to happen, but you have rules that can’t be broken. For example, you might know that a certain event always happens after another event, or that some things just can’t occur together. This paper is about finding ways to understand cause-and-effect relationships even when there are these kinds of constraints. The authors came up with a new way to test whether we can figure out what’s causing something to happen, taking into account all the rules and restrictions. They showed that their approach works well by using examples and comparing it to other methods. |
