Causal Representation Learning with Generative Artificial Intelligence: Application to Images and Texts as Treatments
Speaker: Kosuke Imai (Harvard University)
Tuesday, April 1, 1pm (Irish time)
Room: F301, School of Politics and International Relations, Newman Building
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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.
About the speaker: Kosuke Imai is Professor in the (opens in a new window)Department of Government and the (opens in a new window)Department of Statistics at Harvard University. He is also an affiliate of the (opens in a new window)Institute for Quantitative Social Science. Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference, computational social science, and survey methodology. Imai leads the (opens in a new window)Algorithm-Assisted Redistricting Methodology Project (ALARM) and served as an expert witness for several high-profile legislative redistricting cases. In addition, he is the author of (opens in a new window)Quantitative Social Science: An Introduction (Princeton University Press, 2017). Outside of Harvard, Imai served as the President of the (opens in a new window)Society for Political Methodology from 2017 to 2019.