@inproceedings{canby-etal-2025-reliable,
title = "How Reliable are Causal Probing Interventions?",
author = "Canby, Marc E. and
Davies, Adam and
Rastogi, Chirag and
Hockenmaier, Julia",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.47/",
pages = "857--878",
ISBN = "979-8-89176-298-5",
abstract = "Causal probing aims to analyze foundation models by examining how intervening on their representation of various latent properties impacts their outputs. Recent works have cast doubt on the theoretical basis of several leading causal probing methods, but it has been unclear how to systematically evaluate the effectiveness of these methods in practice. To address this, we define two key causal probing desiderata: *completeness* (how thoroughly the representation of the target property has been transformed) and *selectivity* (how little non-targeted properties have been impacted). We find that there is an inherent tradeoff between the two, which we define as *reliability*, their harmonic mean. We introduce an empirical analysis framework to measure and evaluate these quantities, allowing us to make the first direct comparisons between different families of leading causal probing methods (e.g., linear vs. nonlinear, or concept removal vs. counterfactual interventions). We find that: (1) all methods show a clear tradeoff between completeness and selectivity; (2) more complete and reliable methods have a greater impact on LLM behavior; and (3) nonlinear interventions are almost always more reliable than linear interventions.Our project webpage is available at: [https://ahdavies6.github.io/causal{\_}probing{\_}reliability/](https://ahdavies6.github.io/causal{\_}probing{\_}reliability/)"
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<abstract>Causal probing aims to analyze foundation models by examining how intervening on their representation of various latent properties impacts their outputs. Recent works have cast doubt on the theoretical basis of several leading causal probing methods, but it has been unclear how to systematically evaluate the effectiveness of these methods in practice. To address this, we define two key causal probing desiderata: *completeness* (how thoroughly the representation of the target property has been transformed) and *selectivity* (how little non-targeted properties have been impacted). We find that there is an inherent tradeoff between the two, which we define as *reliability*, their harmonic mean. We introduce an empirical analysis framework to measure and evaluate these quantities, allowing us to make the first direct comparisons between different families of leading causal probing methods (e.g., linear vs. nonlinear, or concept removal vs. counterfactual interventions). We find that: (1) all methods show a clear tradeoff between completeness and selectivity; (2) more complete and reliable methods have a greater impact on LLM behavior; and (3) nonlinear interventions are almost always more reliable than linear interventions.Our project webpage is available at: [https://ahdavies6.github.io/causal_probing_reliability/](https://ahdavies6.github.io/causal_probing_reliability/)</abstract>
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%0 Conference Proceedings
%T How Reliable are Causal Probing Interventions?
%A Canby, Marc E.
%A Davies, Adam
%A Rastogi, Chirag
%A Hockenmaier, Julia
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F canby-etal-2025-reliable
%X Causal probing aims to analyze foundation models by examining how intervening on their representation of various latent properties impacts their outputs. Recent works have cast doubt on the theoretical basis of several leading causal probing methods, but it has been unclear how to systematically evaluate the effectiveness of these methods in practice. To address this, we define two key causal probing desiderata: *completeness* (how thoroughly the representation of the target property has been transformed) and *selectivity* (how little non-targeted properties have been impacted). We find that there is an inherent tradeoff between the two, which we define as *reliability*, their harmonic mean. We introduce an empirical analysis framework to measure and evaluate these quantities, allowing us to make the first direct comparisons between different families of leading causal probing methods (e.g., linear vs. nonlinear, or concept removal vs. counterfactual interventions). We find that: (1) all methods show a clear tradeoff between completeness and selectivity; (2) more complete and reliable methods have a greater impact on LLM behavior; and (3) nonlinear interventions are almost always more reliable than linear interventions.Our project webpage is available at: [https://ahdavies6.github.io/causal_probing_reliability/](https://ahdavies6.github.io/causal_probing_reliability/)
%U https://aclanthology.org/2025.ijcnlp-long.47/
%P 857-878
Markdown (Informal)
[How Reliable are Causal Probing Interventions?](https://aclanthology.org/2025.ijcnlp-long.47/) (Canby et al., IJCNLP-AACL 2025)
ACL
- Marc E. Canby, Adam Davies, Chirag Rastogi, and Julia Hockenmaier. 2025. How Reliable are Causal Probing Interventions?. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 857–878, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.