Amortized Inference for Causal Structure Learning

TL;DR

This paper introduces a variational inference approach to causal structure learning that bypasses traditional search methods, enabling efficient and scalable discovery of causal graphs from data, with strong generalization and performance improvements.

Contribution

It proposes an amortized inference model that directly predicts causal structures, leveraging domain-specific biases learned from simulated data, and generalizes well to larger and shifted datasets.

Findings

Robust generalization to larger problem instances.

Significant performance improvements over existing methods.

Effective in genomics data with distribution shifts.

Abstract

Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is difficult. In this work, we propose to amortize causal structure learning. Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data. This allows our inference model to acquire domain-specific inductive biases for causal discovery solely from data generated by a simulator, bypassing both the hand-engineering of suitable score functions and the search over graphs. The architecture of our inference model emulates permutation invariances that are crucial for statistical efficiency in structure learning, which facilitates generalization to significantly larger problem instances than seen during training. On synthetic data and semisynthetic gene expression data, our models exhibit robust generalization capabilities when subject to substantial distribution shifts and significantly outperform existing algorithms, especially in the challenging genomics domain. Our code and models are publicly available at: https://github.com/larslorch/avici.