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    <abstract>Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect both treatment and outcome, potentially leading to biased causal effects. This bias arises from insufficient consideration of non-confounding covariates, which are relevant only to either the treatment or the outcome. In this work, we aim to mitigate the bias by unveiling interactions between different variables to disentangle the non-confounding covariates when estimating causal effects from text. The disentangling process ensures covariates only contribute to their respective objectives, enabling independence between variables. Additionally, we impose a constraint to balance representations from the treated group and control group to alleviate selection bias. We conduct experiments on two different treatment factors under various scenarios, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis on earnings call transcripts demonstrates that our model can effectively disentangle the variables, and further investigations into real-world scenarios provide guidance for investors to make informed decisions.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.709</identifier>
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%0 Conference Proceedings
%T Causal Inference from Text: Unveiling Interactions between Variables
%A Zhou, Yuxiang
%A He, Yulan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhou-he-2023-causal
%X Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect both treatment and outcome, potentially leading to biased causal effects. This bias arises from insufficient consideration of non-confounding covariates, which are relevant only to either the treatment or the outcome. In this work, we aim to mitigate the bias by unveiling interactions between different variables to disentangle the non-confounding covariates when estimating causal effects from text. The disentangling process ensures covariates only contribute to their respective objectives, enabling independence between variables. Additionally, we impose a constraint to balance representations from the treated group and control group to alleviate selection bias. We conduct experiments on two different treatment factors under various scenarios, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis on earnings call transcripts demonstrates that our model can effectively disentangle the variables, and further investigations into real-world scenarios provide guidance for investors to make informed decisions.
%R 10.18653/v1/2023.findings-emnlp.709
%U https://aclanthology.org/2023.findings-emnlp.709
%U https://doi.org/10.18653/v1/2023.findings-emnlp.709
%P 10559-10571

Markdown (Informal)

[Causal Inference from Text: Unveiling Interactions between Variables](https://aclanthology.org/2023.findings-emnlp.709) (Zhou & He, Findings 2023)

• Causal Inference from Text: Unveiling Interactions between Variables (Zhou & He, Findings 2023)

ACL

• Yuxiang Zhou and Yulan He. 2023. Causal Inference from Text: Unveiling Interactions between Variables. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10559–10571, Singapore. Association for Computational Linguistics.