Learning Neural Causal Models from Unknown Interventions (1910.01075v2)

Abstract: Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides much richer information about the underlying data-generating process. However, the extension and application of methods designed for observational data to include interventions is not straightforward and remains an open problem. In this paper we provide a general framework based on continuous optimization and neural networks to create models for the combination of observational and interventional data. The proposed method is even applicable in the challenging and realistic case that the identity of the intervened upon variable is unknown. We examine the proposed method in the setting of graph recovery both de novo and from a partially-known edge set. We establish strong benchmark results on several structure learning tasks, including structure recovery of both synthetic graphs as well as standard graphs from the Bayesian Network Repository.

• The paper presents a novel Neural Interventional Models framework that efficiently discovers causal graphs from unknown intervention targets.
• The method integrates observational data fitting with interventional graph scoring to infer intervention targets through distribution shifts.
• Experimental results demonstrate robust performance and improved generalization compared to state-of-the-art causal discovery techniques.

Learning Neural Causal Models from Unknown Interventions

The paper "Learning Neural Causal Models from Unknown Interventions" explores an innovative approach in the field of structural causal modeling by introducing a framework that effectively integrates observational and interventional data using continuous optimization and neural networks. The research addresses the challenge of causal structure discovery when the identity of intervened variables remains unknown, a common scenario in real-world settings where interventions can be applied by external agents without explicit disclosure.

Overview of Methodology

At the core of this research is the introduction of Neural Interventional Models (NIMs), leveraging the power of continuous optimization techniques to recover Bayesian network structures under interventions. The paper advances existing methodologies by encompassing cases where the target variable of interventions is uncertain or unknown, which has generally posed significant difficulties due to the lack of theoretical guarantees for identifying true causal graphs in such scenarios.

The proposed framework operates in three distinct phases:

1. Graph Fitting on Observational Data: This phase involves training functional parameters of the model to match samples from the observational data distribution. The use of graph configuration samples allows the model to account for uncertainties in the underlying causal structure.
2. Graph Scoring on Interventional Data: The model assesses multiple graph configurations by examining their performance on data obtained under unknown interventions. A novelty here is the algorithm's ability to infer which variable was most likely targeted by an intervention through analyzing shifts in distributions.
3. Credit Assignment to Structural Parameters: Utilizing a gradient estimator analogous to REINFORCE, the model updates its structural parameters to align closer with the most probable causal structure.

Experimental Results

The framework is meticulously evaluated across synthetic and real-world datasets, yielding robust performance even on challenging graph recovery tasks from the Bayesian Network Repository. Some of the salient findings include:

• Benchmark Performance: The framework demonstrates superior benchmark results, outperforming state-of-the-art causal structure discovery methods, particularly in recovering causal graphs from interventional data where traditional methods fall short.
• Generalization Abilities: Highlighting the proposed method's strength, it maintains performance even under previously unseen interventions, pointing towards a promising avenue for adaptability and robustness in changing environments.
• Partial Graph Recovery: The model adeptly handles scenarios where part of the graph structure is given, showcasing its flexibility and application in domains like protein interaction networks.

Implications and Future Directions

The implications of this research stretch across both theoretical advances in causal inference and practical applications involving AI systems required to understand and adapt to complex causal relationships. The ability to learn causal structures from unknown interventions can significantly enhance AI systems' ability to make informed predictions and decisions in dynamic environments.

Looking forward, the integration of Neural Interventional Models with emerging neural architectures and further enhancements in scalability and data efficiency hold potential for expanding the applicability of this approach. Moreover, exploring meta-learning frameworks to fine-tune causal models in real-time, further aligning with real-world complexities, could serve as a fruitful research direction.

Through this comprehensive evaluation, the paper substantially contributes to the ongoing developments in neural causal modeling, setting the stage for more robust AI systems capable of inferring and leveraging complex causal structures.