2023
DOI: 10.48550/arxiv.2303.02320
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Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

Abstract: Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc. Real-world time series can include large-scale, irregular, and intermittent time series observations, raising significant challenges to existing work attempting to estimate treatment effects. Specifically, the existence of hidden confounders can lead to biased treatment estimates and complicate the causal inference process… Show more

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