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arXiv · 2510.14073

Exploratory Causal Inference in SAEnce

Tommaso Mencattini·Riccardo Cadei·Francesco Locatello
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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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Tommaso Mencattini, Riccardo Cadei, Francesco Locatello. 2025-10-15. Exploratory Causal Inference in SAEnce. https://arxiv.org/abs/2510.14073

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