Interpretable Causal Discovery via Causal-Effect Constraints

2026-08-12

AI Summary

This work addresses the problem of inferring interpretable causal graphs from data under prescribed constraints on causal effects. The authors formulate constrained causal discovery as a conditional Bayesian inference task, aiming to estimate the posterior distribution over causal graphs and parameters given specific causal effect conditions—such as requiring large effects. To tackle the computational challenges posed by low-probability constraint regions, they innovatively integrate rare-event estimation techniques with particle filtering and graph structure learning. Experimental results demonstrate that the proposed method achieves both accuracy and scalability on synthetic data, and successfully generates biologically meaningful pathway-level causal summaries on the Sachs protein signaling dataset, substantially enhancing the interpretability and practical utility of causal discovery.

Abstract

Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but also to be able to interpret and explain either observed or hypothesized phenomena, such as a particularly large causal effect. We consider this task of conditional causal discovery and cast it as a Bayesian inference problem, in which we target the posterior over causal graphs and parameters conditional on an event such as a causal-effect constraint. Unfortunately, this poses a computational challenge: existing approaches to Bayesian causal discovery struggle when the event has small posterior mass. To address this, we adapt rare-event estimation techniques to perform inference the joint graph-parameter space. Our method gradually drives a particle population toward the constrained region while maintaining samples that approximate the conditional posterior. Empirical evaluation on synthetic graphs validates the accuracy of our approach at small and large scales, and we show in a case study on the Sachs protein dataset how our method can be used to aid scientific exploration by providing pathway-level summaries.

Problem

• causal discovery
• causal-effect constraints
• Bayesian inference
• rare-event estimation
• conditional posterior

Innovation

• causal discovery
• Bayesian inference
• causal-effect constraints
• rare-event estimation
• interpretable AI