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        <title>CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation</title>
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        <namePart type="given">Dibyanayan</namePart>
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        <namePart type="given">Soham</namePart>
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    <name type="personal">
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    <name type="personal">
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    <abstract>Multimodal classifiers function as opaque black box models. While several techniques exist to interpret their predictions, very few of them are as intuitive and accessible as natural language explanations (NLEs). To build trust, such explanations must faithfully capture the classifier’s internal decision making behavior, a property known as faithfulness. In this paper, we propose CAuSE (Causal Abstraction under Simulated Explanations), a novel framework to generate faithful NLEs for any pretrained multimodal classifier. We demonstrate that CAuSE generalizes across datasets and models through extensive empirical evaluation. Theoretically, we show that CAuSE, trained via interchange intervention, forms a causal abstraction of the underlying classifier. We further validate this through a redesigned metric for measuring causal faithfulness in multimodal settings. CAuSE surpasses other methods on this metric, with qualitative analysis reinforcing its advantages. We also perform detailed error analysis to pinpoint the failure cases of CAuSE1.</abstract>
    <identifier type="citekey">bandyopadhyay-etal-2026-cause</identifier>
    <identifier type="doi">10.1162/tacl.a.686</identifier>
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        <date>2026</date>
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%0 Journal Article
%T CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation
%A Bandyopadhyay, Dibyanayan
%A Bhattacharjee, Soham
%A Hasanuzzaman, Mohammed
%A Ekbal, Asif
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F bandyopadhyay-etal-2026-cause
%X Multimodal classifiers function as opaque black box models. While several techniques exist to interpret their predictions, very few of them are as intuitive and accessible as natural language explanations (NLEs). To build trust, such explanations must faithfully capture the classifier’s internal decision making behavior, a property known as faithfulness. In this paper, we propose CAuSE (Causal Abstraction under Simulated Explanations), a novel framework to generate faithful NLEs for any pretrained multimodal classifier. We demonstrate that CAuSE generalizes across datasets and models through extensive empirical evaluation. Theoretically, we show that CAuSE, trained via interchange intervention, forms a causal abstraction of the underlying classifier. We further validate this through a redesigned metric for measuring causal faithfulness in multimodal settings. CAuSE surpasses other methods on this metric, with qualitative analysis reinforcing its advantages. We also perform detailed error analysis to pinpoint the failure cases of CAuSE1.
%R 10.1162/tacl.a.686
%U https://aclanthology.org/2026.tacl-1.37/
%U https://doi.org/10.1162/tacl.a.686
%P 829-851

Markdown (Informal)

[CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation](https://aclanthology.org/2026.tacl-1.37/) (Bandyopadhyay et al., TACL 2026)

• CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation (Bandyopadhyay et al., TACL 2026)

ACL

• Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Mohammed Hasanuzzaman, and Asif Ekbal. 2026. CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation. Transactions of the Association for Computational Linguistics, 14:829–851.