Differentiable Causal Discovery from Interventional Data
About
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Causal Discovery | Synthetic Data | Runtime837.8 | 57 | |
| Causal Structure Learning | Sachs | SHD21 | 38 | |
| Causal Discovery | Semantic Causal Environment observation-only | F1 Score33.8 | 15 | |
| Graph Recovery | Synthetic Nonlinear SEM Gaussian Noise (test) | AUPRC20.4 | 15 | |
| Graph Recovery | Synthetic Nonlinear SEM Exponential Noise (test) | AUPRC15.8 | 15 | |
| Graph Recovery | Synthetic Nonlinear SEM (Gumbel Noise) (test) | AUPRC0.109 | 15 | |
| Causal Discovery | Synthetic Data Observation-only (1000 samples) | Rank13 | 15 | |
| Causal Discovery | K562 Perturb-seq CausalBench | W Score18.3 | 13 | |
| Causal Discovery | RPE1 Perturb-seq CausalBench | W Score19.4 | 13 | |
| Causal Discovery | SACHS p = 11, s = 20, n = 100 (real flow cytometry) | F1 Score22 | 13 |