Summary of Scalable Variational Causal Discovery Unconstrained by Acyclicity, By Nu Hoang et al.
Scalable Variational Causal Discovery Unconstrained by Acyclicity
by Nu Hoang, Bao Duong, Thin Nguyen
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 presents a scalable Bayesian approach for learning the posterior distribution over causal graphs given observational data. The method efficiently generates directed acyclic graphs (DAGs) without enforcing acyclicity, allowing it to effectively model epistemic uncertainties among various structurally diverse causal theories. The proposed differentiable DAG sampling scheme maps an unconstrained distribution of topological orders to a distribution over DAGs, enabling the use of variational inference for modeling the posterior distribution. Empirical experiments on simulated and real datasets demonstrate the superior performance of the proposed model compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how things can cause each other by creating a new way to learn about causal relationships in data. The method is special because it’s very efficient at generating different possibilities for how these relationships might work. This allows us to model the uncertainty of what we’re learning, which is important when dealing with complex real-world problems. The approach was tested on both fake and real datasets and performed better than other methods. |
Keywords
* Artificial intelligence * Inference
