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We consider structure-agnostic causal inference that estimates treatment effect estimation using black-box ML estimates of nuisance functions, and show that the celebrated DML is optimal when the treatment noise is Gaussian. When the noise is non-Gaussian, we propose ACE, a novel class of higher-order structure-agnostic estimators.

We propose a causal representation learning framework to understand the hierarchical structure of language model capabilities, revealing causal relationships between general problem-solving, instruction-following, and mathematical reasoning abilities.

We build a novel benchmark for evaluating how well can state-of-the-art LLMs solve inequality proving problems. Our evaluation studies provide insights on their advanced math reasoning capabilities and highlight their common reasoning flaws.