aiqbench analysis
This study exposes a critical reproducibility gap in causal representation learning (CRL), demonstrating that current state-of-the-art methods fail to recover underlying causal factors even on simple, controlled optical experiments. The findings highlight that common theoretical assumptions regarding mixing functions often do not hold in real-world data, challenging the practical applicability of these algorithms. This benchmark serves as an essential sanity check for researchers aiming to bridge the divide between theoretical promises and functional CRL methodologies.
Sanity Checking Causal Representation Learning on a Simple Real-World System
arXiv:2502.20099v3 Announce Type: replace-cross Abstract: We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The system consists of a controlled optical experiment producing a variety of measurements where the underlying causal factors---the control inputs to the experiment---are known, providing a ground truth. We select methods representative of different approaches to CRL and find that they all fail to consistently recover the underlying causal factors. To understand the failure modes of the evaluated algorithms, we perform an ablation on the data by substituting the real data-generating process with a simpler synthetic equivalent. The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process. Additionally, we observe that common assumptions on the mixing function are crucial for the performance of some of the methods but do not hold in the real data. Our efforts highlight the contrast between the theoretical promise of the state of the art and the challenges in its application. We hope the benchmark serves as a simple, real-world sanity check to further develop and validate methodol- ogy, bridging the gap towards CRL methods that work in practice.
Abstract:We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The system consists of a controlled optical experiment producing a variety of measurements where the underlying causal factors---the control inputs to the experiment---are known, providing a ground truth. We select methods representative of different approaches to CRL and find that they all fail to consistently recover the underlying causal factors. To understand the failure modes of the evaluated algorithms, we perform an ablation on the data by substituting the real data-generating process with a simpler synthetic equivalent. The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process. Additionally, we observe that common assumptions on the mixing function are crucial for the performance of some of the methods but do not hold in the real data. Our efforts highlight the contrast between the theoretical promise of the state of the art and the challenges in its application. We hope the benchmark serves as a simple, real-world sanity check to further develop and validate methodol- ogy, bridging the gap towards CRL methods that work in practice.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME)
arXiv:2502.20099 [cs.LG]
(or arXiv:2502.20099v3 [cs.LG] for this version)
From: Juan L. Gamella [view email] [v1] Thu, 27 Feb 2025 13:56:54 UTC (3,637 KB) [v2] Mon, 28 Apr 2025 10:30:46 UTC (3,637 KB) [v3] Mon, 14 Sep 2026 09:25:20 UTC (3,637 KB)
Full text under the publisher's open licence · 244 words
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