Papers by Lucius E.j. Bynum
Language Models as Causal Effect Generators (2025.emnlp-main)
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| Challenge: | Using sequence-driven structural causal models (SD-SCMs) we characterize how SD-SCAMs enables sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. |
| Approach: | They propose a sequence-driven structural causal model that uses language models to parameterize a structural causal system based on a user-specified DAG. |
| Outcome: | The proposed method outperforms state-of-the-art methods and can underpin auditing of language models for (un)desirable causal effects, such as misinformation or discrimination. |