Summary of Cross-validating Causal Discovery Via Leave-one-variable-out, by Daniela Schkoda et al.
Cross-validating causal discovery via Leave-One-Variable-Out
by Daniela Schkoda, Philipp Faller, Patrick Blöbaum, Dominik Janzing
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 The proposed approach falsifies causal discovery algorithms without ground truth by testing the causal model on a pair of variables dropped during learning. The Leave-One-Variable-Out (LOVO) prediction infers Y from X based on training data and joint observations of X and Z1, …, Zn, and Yi, respectively. Causal models are constructed using Acyclic Directed Mixed Graphs (ADMGs), enabling predictions about dependencies between X and Y. The prediction error can be estimated since the joint distribution PX, Y is available, with X and Y omitted for falsification purposes. This graphical method applies to general causal discovery algorithms, and a LOVO predictor is tailored towards specific a priori assumptions like linear additive noise models. Simulations show that the LOVO prediction error correlates with the accuracy of causal outputs, affirming the method’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to test if a computer program that discovers causes is actually correct or not. It does this by looking at how well the program works when it’s given information about one thing but not another. This helps us figure out if the program is making mistakes and why. The researchers used special graphs called Acyclic Directed Mixed Graphs (ADMGs) to understand how the program is working and what it’s learning from the data. They tested this method on some sample data and found that it can help us identify when a program is not doing its job correctly. |
