Differentiable Causal Discovery from Interventional Data
This work addresses causal discovery for researchers and practitioners in machine learning, offering a flexible approach that improves upon existing methods by incorporating interventional data, though it is incremental in advancing continuous-constrained optimization frameworks.
The paper tackles the problem of learning causal directed acyclic graphs from data by proposing a theoretically-grounded method based on neural networks that leverages interventional data to alleviate identifiability issues, showing favorable comparisons to state-of-the-art methods in various settings including perfect and imperfect interventions.
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.