Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments

2024-10-01

arXiv.org

AI Summary

Causal inference with high-dimensional unstructured textual covariates—e.g., clinical notes and patient feedback—remains challenging due to the difficulty of identifying valid treatment representations. Method: We propose a novel paradigm for causal effect identification that directly leverages frozen, intrinsic semantic representations (e.g., sentiment, topic) from large language models (LLMs), specifically Llama 3, as treatment features—bypassing data-driven learning of implicit causal representations and enabling perception-driven causal modeling and text reuse. Contribution/Results: We establish nonparametric identifiability of the average treatment effect (ATE) under this framework and prove asymptotic optimality under double machine learning. Experiments on synthetic and real-world datasets demonstrate significant improvements in estimation accuracy and computational efficiency, while robustly mitigating violations of the overlap assumption.

Technology Category

• Machine Learning: Causal Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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. 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 specific sentiments and certain topics, from other possibly unknown confounding features. Unlike the existing methods, our proposed approach eliminates the need to learn causal representation from the data and hence produces more accurate and efficient estimates. We formally establish the conditions required for the nonparametric identification of the average treatment effect, propose an estimation strategy that avoids the violation of the overlap assumption, and derive the asymptotic properties of the proposed estimator through the application of double machine learning. Finally, using an instrumental variables approach, we extend the proposed methodology to the settings, in which the treatment feature is based on human perception rather than is assumed to be fixed given the treatment object. The proposed methodology is also applicable to text reuse where an LLM is used to regenerate the existing texts. We conduct simulation and empirical studies, using the generated text data from an open-source LLM, Llama 3, to illustrate the advantages of our estimator over the state-of-the-art causal representation learning algorithms.

Problem

• Enhancing causal inference validity with unstructured text treatments
• Using GenAI to disentangle treatment features from confounders
• Extending methodology to human-perceived treatment features

Innovation

• Uses LLMs to generate treatments for causal inference
• Leverages GenAI to disentangle treatment features
• Applies double machine learning for estimator properties