Hybrid Causal Identification and Causal Mechanism Clustering

TL;DR

This paper introduces a novel framework combining mixture models and neural networks to identify heterogeneous causal mechanisms from observational data collected across different environments.

Contribution

It proposes the MCVCI and MCVCC models that leverage probabilistic bounds and clustering to infer and reveal diverse causal mechanisms, improving over existing methods.

Findings

Outperforms state-of-the-art methods on simulated data

Effective in revealing causal heterogeneity

Demonstrates robustness on real-world datasets

Abstract

Bivariate causal direction identification is a fundamental and vital problem in the causal inference field. Among binary causal methods, most methods based on additive noise only use one single causal mechanism to construct a causal model. In the real world, observations are always collected in different environments with heterogeneous causal relationships. Therefore, on observation data, this paper proposes a Mixture Conditional Variational Causal Inference model (MCVCI) to infer heterogeneous causality. Specifically, according to the identifiability of the Hybrid Additive Noise Model (HANM), MCVCI combines the superior fitting capabilities of the Gaussian mixture model and the neural network and elegantly uses the likelihoods obtained from the probabilistic bounds of the mixture conditional variational auto-encoder as causal decision criteria. Moreover, we model the casual heterogeneity into cluster numbers and propose the Mixture Conditional Variational Causal Clustering (MCVCC) method, which can reveal causal mechanism expression. Compared with state-of-the-art methods, the comprehensive best performance demonstrates the effectiveness of the methods proposed in this paper on several simulated and real data.