Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments
arXiv:2410.00903v5 Announce Type: replace-cross Abstract: In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation helps disentangle the treatment features of interest, such as sp
The rapid advancement and widespread availability of generative AI models, particularly LLMs, create an immediate opportunity to apply these new capabilities to existing challenges in causal inference.
Improving the accuracy and validity of causal inference, especially with complex unstructured data like text, is critical for informed decision-making in diverse fields from policy to product development.
The ability to accurately attribute causality in scenarios involving textual 'treatments' allows for more precise understanding of impact and intervention, moving beyond correlation-based analyses.
- · Researchers (social sciences, health, economics)
- · AI/ML developers
- · Data scientists
- · Evidence-based policy makers
- · Organizations relying solely on correlational analyses
- · Traditional causal inference methodologies with high dimensionality
- · Businesses making decisions based on imprecise causal links
More accurate causal models will be developed across various domains using textual data.
Improved understanding of the causal effects of communication, narratives, and information on human behavior and societal outcomes.
Enhanced AI systems capable of not just processing information, but understanding and manipulating its causal implications to achieve desired effects.
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