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Estimating Treatment Effects in Continuous Time with Hidden Confounders
Defu Cao · James Enouen · Yan Liu

Estimating individual treatment effects (ITEs) plays a crucial role in many real-world applications involving policy analysis and decision making. Nevertheless, estimating treatment effects in the longitudinal setting in the presence of hidden confounders remains an extremely challenging problem. Recently, there is a growing body of work attempting to obtain unbiased ITE estimates from time-dynamic observational data by ignoring the possible existence of hidden confounders. Additionally, many existing works handling hidden confounders are not applicable for continuous-time settings.In this paper, we extend the line of work focusing on deconfounding in the dynamic time setting in the presence of hidden confounders. We leverage recent advancements in neural differential equations to build a latent factor model using a stochastic controlled differential equation and Lipschitz constrained convolutional operation in order to continuously incorporate information about ongoing interventions and irregularly sampled observations. Experiments on both synthetic and real-world datasets highlight the promise of continuous time methods for estimating treatment effects in the presence of hidden confounders.

Author Information

Defu Cao (USC)

Defu Cao is a Ph.D. student in Computer Science at the University of Southern California, working with Prof. Yan Liu at the USC Melady Lab. Prior, he earned his Master's Degree at school of EECS Peking University where he was co-advised by Prof. Xu Cheng and Prof. Xianhua Liu. Cao is primarily interested in developing machine learning and data mining algorithms that demonstrate a deep understanding of the world with special structures, including time series, spatio-temporal data, and relational data. To this end, his research aims to integrate causal inference, graph neural networks, spectral domain representation, interpretability, and robustness, he is also interested in multi-task learning and pre-training model in the NLP domain. He has published his research in top conference proceedings including NeurIPS, ICRA, ICDM, PAKDD, NAACL, and TrustCom.

James Enouen (University of Southern California)
Yan Liu (University of Southern California)

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