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cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
Demistifying Data and Simulator Assumptions in Supervised Causal Discovery
TL;DR
Supervised causal discovery methods leverage simulated training data to infer causal structures in new datasets. This paper clarifies how assumptions from simulators impact predictions and highlights the need for careful evaluation of these methods.
✦ Why It Matters
Evaluate your causal discovery methods against identifiable targets to ensure robust predictions in real-world scenarios.
Key Takeaways
- Use simulated data carefully to understand causal assumptions.
- Evaluate methods against identifiable causal targets.
- Document external causal evidence for benchmark graphs.
- Test for generalizability under varying conditions.
How It Works
Supervised causal discovery methods utilize training datasets with known causal structures to learn how to infer causal relationships in new datasets. By organizing these methods based on their prediction targets and training approaches, the paper reveals how different assumptions about mechanisms and noise can affect the predictions made.
This structured analysis helps clarify the relationship between the training data and the causal structures inferred.