Summary of Continuous Bayesian Model Selection For Multivariate Causal Discovery, by Anish Dhir et al.
Continuous Bayesian Model Selection for Multivariate Causal Discovery
by Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker, Mark van der Wilk
First submitted to arxiv on: 15 Nov 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 This paper introduces a new approach to multivariate causal discovery that relaxes restrictive model assumptions, making it more accurate and applicable to real-world scenarios. The authors extend a Bayesian model selection method, previously shown effective for bivariate cases, to the multivariate setting by introducing a continuous relaxation of the large discrete selection problem. The approach uses a non-parametric Bayesian model, the Causal Gaussian Process Conditional Density Estimator (CGP-CDE), which constructs an adjacency matrix from hyperparameters and optimizes it using marginal likelihood and acyclicity regularizers. The resulting maximum a posteriori causal graph is demonstrated to be competitive on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps us figure out how different things are related when we don’t have all the information. It’s like trying to solve a puzzle without having all the pieces. This method can help us make better guesses about what’s connected and what isn’t, even if we don’t know everything. The authors show that their approach works well on both fake and real data. |
Keywords
* Artificial intelligence * Likelihood
