Amortized Inference for Causal Structure Learning

by   Lars Lorch, et al.

Learning causal structure poses a combinatorial search problem that typically involves evaluating structures using 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 the process of causal structure learning. Rather than searching over causal structures directly, we train a variational inference model to predict the causal structure from observational/interventional data. Our inference model acquires domain-specific inductive bias for causal discovery solely from data generated by a simulator. This allows us to bypass both the search over graphs and the hand-engineering of suitable score functions. Moreover, the architecture of our inference model is permutation invariant w.r.t. the data points and permutation equivariant w.r.t. the variables, facilitating generalization to significantly larger problem instances than seen during training. On synthetic data and semi-synthetic gene expression data, our models exhibit robust generalization capabilities under substantial distribution shift and significantly outperform existing algorithms, especially in the challenging genomics domain.


Learning to Induce Causal Structure

The fundamental challenge in causal induction is to infer the underlying...

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Learning causal structures from observation and experimentation is a cen...

Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning

Inference of causal structures from observational data is a key componen...

Ancestral Causal Inference

Constraint-based causal discovery from limited data is a notoriously dif...

Learning Causal Models Online

Predictive models – learned from observational data not covering the com...

Supervised Whole DAG Causal Discovery

We propose to address the task of causal structure learning from data in...

Constraint-Based Causal Structure Learning from Undersampled Graphs

Graphical structures estimated by causal learning algorithms from time s...

Please sign up or login with your details

Forgot password? Click here to reset