Score matching enables causal discovery of nonlinear additive noise models
About
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-the-art causal discovery methods while being significantly faster.
Paul Rolland, Volkan Cevher, Matth\"aus Kleindessner, Chris Russel, Bernhard Sch\"olkopf, Dominik Janzing, Francesco Locatello• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Causal Discovery | Sachs real-world data protein signaling network | SHD12 | 41 | |
| Causal Structure Learning | Sachs | SHD12 | 38 | |
| Causal Discovery | Syntren | F1 Score18.3 | 22 | |
| Causal Discovery | SF5 (n=30, h=5) | FDR55 | 18 | |
| Causal Discovery | ER5 (n=30, h=5) | FDR0.66 | 18 | |
| Causal Discovery | Synthetic ER3 n=50, h=3 (test) | FDR69 | 17 | |
| Causal Discovery | Synthetic SF3 n=50, h=3 (test) | FDR64 | 17 | |
| Causal Discovery | Causal Discovery Suite 30% MAR missingness | Coverage15 | 16 | |
| Causal Discovery | 15 Real-world Causal Discovery Benchmark Datasets | Coverage15 | 16 | |
| Causal Discovery | FoundCause 10% MAR missingness | AUROC0.521 | 16 |
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