Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Jing Huang, Junyi Tao, Thomas Icard, Diyi Yang, Christopher Potts
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:25791-25812, 2025.

Abstract

Interpretability research now offers a variety of techniques for identifying abstract internal mechanisms in neural networks. Can such techniques be used to predict how models will behave on out-of-distribution examples? In this work, we provide a positive answer to this question. Through a diverse set of language modeling tasks—including symbol manipulation, knowledge retrieval, and instruction following—we show that the most robust features for correctness prediction are those that play a distinctive causal role in the model’s behavior. Specifically, we propose two methods that leverage causal mechanisms to predict the correctness of model outputs: counterfactual simulation (checking whether key causal variables are realized) and value probing (using the values of those variables to make predictions). Both achieve high AUC-ROC in distribution and outperform methods that rely on causal-agnostic features in out-of-distribution settings, where predicting model behaviors is more crucial. Our work thus highlights a novel and significant application for internal causal analysis of language models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-huang25af, title = {Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors}, author = {Huang, Jing and Tao, Junyi and Icard, Thomas and Yang, Diyi and Potts, Christopher}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {25791--25812}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/huang25af/huang25af.pdf}, url = {https://proceedings.mlr.press/v267/huang25af.html}, abstract = {Interpretability research now offers a variety of techniques for identifying abstract internal mechanisms in neural networks. Can such techniques be used to predict how models will behave on out-of-distribution examples? In this work, we provide a positive answer to this question. Through a diverse set of language modeling tasks—including symbol manipulation, knowledge retrieval, and instruction following—we show that the most robust features for correctness prediction are those that play a distinctive causal role in the model’s behavior. Specifically, we propose two methods that leverage causal mechanisms to predict the correctness of model outputs: counterfactual simulation (checking whether key causal variables are realized) and value probing (using the values of those variables to make predictions). Both achieve high AUC-ROC in distribution and outperform methods that rely on causal-agnostic features in out-of-distribution settings, where predicting model behaviors is more crucial. Our work thus highlights a novel and significant application for internal causal analysis of language models.} }
Endnote
%0 Conference Paper %T Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors %A Jing Huang %A Junyi Tao %A Thomas Icard %A Diyi Yang %A Christopher Potts %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-huang25af %I PMLR %P 25791--25812 %U https://proceedings.mlr.press/v267/huang25af.html %V 267 %X Interpretability research now offers a variety of techniques for identifying abstract internal mechanisms in neural networks. Can such techniques be used to predict how models will behave on out-of-distribution examples? In this work, we provide a positive answer to this question. Through a diverse set of language modeling tasks—including symbol manipulation, knowledge retrieval, and instruction following—we show that the most robust features for correctness prediction are those that play a distinctive causal role in the model’s behavior. Specifically, we propose two methods that leverage causal mechanisms to predict the correctness of model outputs: counterfactual simulation (checking whether key causal variables are realized) and value probing (using the values of those variables to make predictions). Both achieve high AUC-ROC in distribution and outperform methods that rely on causal-agnostic features in out-of-distribution settings, where predicting model behaviors is more crucial. Our work thus highlights a novel and significant application for internal causal analysis of language models.
APA
Huang, J., Tao, J., Icard, T., Yang, D. & Potts, C.. (2025). Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:25791-25812 Available from https://proceedings.mlr.press/v267/huang25af.html.

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