A GenAI-powered causal inference framework uses deep generative model representations as learned deconfounders to identify dynamic causal effects of video features on real-time outcomes.
arXiv preprint arXiv:2507.03897 , year=
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abstract
We introduce GenAI-Powered Inference (GPI), a statistical framework for both causal and predictive inference using unstructured data, including text and images. GPI leverages open-source Generative Artificial Intelligence (GenAI) models -- such as large language models and diffusion models -- not only to generate unstructured data at scale but also to extract low-dimensional representations that are guaranteed to capture their underlying structure. Applying machine learning to these representations, GPI enables estimation of causal and predictive effects while quantifying associated estimation uncertainty. Unlike existing approaches to representation learning, GPI does not require fine-tuning of generative models, making it computationally efficient and broadly accessible. We illustrate the versatility of the GPI framework through three applications: (1) analyzing Chinese social media censorship, (2) estimating predictive effects of candidates' facial appearance on electoral outcomes, and (3) assessing the persuasiveness of political rhetoric. An open-source software package is available for implementing GPI.
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2026 2representative citing papers
A GenAI-based method extracts representations from unstructured data and uses a neural network to fit marginal structural models that recover causal effects of treatment feature sequences including their positions.
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A GenAI-powered causal inference framework uses deep generative model representations as learned deconfounders to identify dynamic causal effects of video features on real-time outcomes.
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