Sample, estimate, aggregate: A recipe for causal discovery foundation models
This addresses the problem of unreliable causal inference in fields like biology, where data are sparse, but it is incremental as it builds on existing causal discovery methods.
The paper tackles the brittleness of causal discovery algorithms on large variable sets with limited data by proposing a foundation model trained on synthetic data to predict causal graphs from summary statistics. The model generalizes well beyond its training set, handles graphs with hundreds of variables in seconds, and adapts to different data assumptions.
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving perturbations. However, causal discovery algorithms over larger sets of variables tend to be brittle against misspecification or when data are limited. For example, single-cell transcriptomics measures thousands of genes, but the nature of their relationships is not known, and there may be as few as tens of cells per intervention setting. To mitigate these challenges, we propose a foundation model-inspired approach: a supervised model trained on large-scale, synthetic data to predict causal graphs from summary statistics -- like the outputs of classical causal discovery algorithms run over subsets of variables and other statistical hints like inverse covariance. Our approach is enabled by the observation that typical errors in the outputs of a discovery algorithm remain comparable across datasets. Theoretically, we show that the model architecture is well-specified, in the sense that it can recover a causal graph consistent with graphs over subsets. Empirically, we train the model to be robust to misspecification and distribution shift using diverse datasets. Experiments on biological and synthetic data confirm that this model generalizes well beyond its training set, runs on graphs with hundreds of variables in seconds, and can be easily adapted to different underlying data assumptions.