Variation-based Cause Effect Identification

Mohamed Amine Ben Salem, Karim Said Barsim, Bin Yang

Keywords: Causality, Causal Inference, Causal Discovery, Cause Effect Identification, Convex Optimization, Semi-definite Relaxation

TL;DR: A framework for causal discovery in bivariate systems based on realization of the independence of causal mechanisms postulate using convex-optimization

Abstract: Mining genuine mechanisms underlying the complex data generation process in real-world systems is a fundamental step in promoting interpretability of (and thus trust in) data-driven models. Therefore, we propose a variation-based cause effect \underline{i}dentification (VCEI) framework for causal discovery in bivariate systems from a single observational setting. Our framework relies on the principle of independence of cause and mechanism (ICM) under the assumption of an existing acyclic causal link, and offers a practical realization of this principle. Principally, we artificially construct two settings in which the marginal distributions of one covariate, claimed to be the cause, are guaranteed to have non-negligible variations. This is achieved by re-weighting samples of the marginal so that the resultant distribution is notably distinct from this marginal according to some discrepancy measure. In the causal direction, such variations are expected to have no impact on the effect generation mechanism. Therefore, quantifying the impact of these variations on the conditionals reveals the genuine causal direction. Moreover, we formulate our approach in the kernel-based maximum mean discrepancy, lifting all constraints on the data types of cause and effect covariates, and rendering such artificial interventions a convex optimization problem. We provide a series of experiments on real and synthetic data showing that VCEI is, in principle, competitive to other cause effect identification frameworks.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Probabilistic Methods (eg, variational inference, causal inference, Gaussian processes)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2211.12016/code)