Summary of Learning Counterfactual Distributions Via Kernel Nearest Neighbors, by Kyuseong Choi et al.
Learning Counterfactual Distributions via Kernel Nearest Neighbors
by Kyuseong Choi, Jacob Feitelberg, Caleb Chin, Anish Agarwal, Raaz Dwivedi
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel framework for estimating multivariate distributions in settings with missing data and unobserved confounding. It tackles these challenges by introducing a distributional matrix completion approach and a kernel-based distributional generalization of nearest neighbors. The proposed method leverages maximum mean discrepancies and a factor model on kernel mean embeddings to recover underlying distributions, even when data is missing not at random or positivity constraints are violated. Furthermore, the approach is shown to be robust to heteroscedastic noise, provided multiple measurements are available for observed unit-outcome entries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn about how people behave and make choices in different situations. Imagine trying to understand how people spend money on mobile apps or engage with certain features. But sometimes we don’t have all the data, and that makes it hard to figure out what’s going on. The authors of this paper came up with a new way to fill in those gaps using math and computer science. They tested their idea and showed that it works well even when there’s noise or missing information. |
Keywords
» Artificial intelligence » Generalization
