Exploratory Causal Inference in SAEnce

• URL: http://arxiv.org/abs/2510.14073v1
• Date: Wed, 15 Oct 2025 20:30:54 GMT
• Title: Exploratory Causal Inference in SAEnce
• Authors: Tommaso Mencattini, Riccardo Cadei, Francesco Locatello,
• Abstract summary: We propose to discover the unknown effects of a treatment directly from data.<n>For this, we turn unstructured data from a trial into meaningful representations via pretrained foundation models and interpret them via a sparse autoencoder.<n>Discovering significant causal effects at the neural level is not trivial due to multiple-testing issues and effects entanglement.
• Score: 25.91637307089553
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring on popular yet incomplete hypotheses. We propose to discover the unknown effects of a treatment directly from data. For this, we turn unstructured data from a trial into meaningful representations via pretrained foundation models and interpret them via a sparse autoencoder. However, discovering significant causal effects at the neural level is not trivial due to multiple-testing issues and effects entanglement. To address these challenges, we introduce Neural Effect Search, a novel recursive procedure solving both issues by progressive stratification. After assessing the robustness of our algorithm on semi-synthetic experiments, we showcase, in the context of experimental ecology, the first successful unsupervised causal effect identification on a real-world scientific trial.

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