Summary of Probabilities Of Causation For Continuous and Vector Variables, by Yuta Kawakami et al.
Probabilities of Causation for Continuous and Vector Variables
by Yuta Kawakami, Manabu Kuroki, Jin Tian
First submitted to arxiv on: 30 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 paper extends the concept of probabilities of causation (PoC) to continuous treatment and outcome variables, allowing for more nuanced decision-making in explainable artificial intelligence applications. The authors generalize PoC to capture causal effects between multiple treatments and outcomes, as well as consider sub-populations and multi-hypothetical terms. Nonparametric identification theorems are provided for each type of PoC, and a real-world dataset on education is used to illustrate the application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how things might have turned out differently if certain events had occurred. It takes ideas that were originally developed for simple situations and makes them work for more complex scenarios. The authors show how we can use these ideas to make better decisions, even when there are many factors involved. They also apply their results to a real-life example about education. |
