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In previous chapters, one of the biggest challenges in causal inference [1] with observational data is dealing with selection bias. In many real-world datasets, the probability of receiving a treatment is not random; it depends on observed or unobserved characteristics of the individuals.

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Balancing Representations with Causal Deep Learning (CFRNet)

  • 章节

  • 发表日期：

    2025年

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  • Durai Rajamanickam

摘要

In previous chapters, one of the biggest challenges in causal inference [1] with observational data is dealing with selection bias. In many real-world datasets, the probability of receiving a treatment is not random; it depends on observed or unobserved characteristics of the individuals.