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Causal Effect Estimation: Basic Methodologies

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Abstract

In this chapter, we provide a comprehensive review of causal inference methods for the causal effect estimation task under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. Most contents in this chapter are reprinted from our work (Yao et al. (ACM Trans Knowl Discov Data 15(5):1–46, 2021)).

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Authors and Affiliations

  1. Alibaba Group, Hangzhou, China

    Liuyi Yao & Yaliang Li

  2. Ant Group, Hangzhou, China

    Zhixuan Chu

  3. Purdue University, West Lafayette, IN, USA

    Jing Gao

  4. University of Virginia, Charlottesville, VA, USA

    Aidong Zhang & Sheng Li

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  1. Liuyi Yao
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  2. Zhixuan Chu
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  3. Yaliang Li
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  4. Jing Gao
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  6. Sheng Li
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Correspondence to Sheng Li .

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Editors and Affiliations

  1. University of Virginia, Charlottesville, VA, USA

    Sheng Li

  2. Ant Group, Hangzhou, China

    Zhixuan Chu

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Yao, L., Chu, Z., Li, Y., Gao, J., Zhang, A., Li, S. (2023). Causal Effect Estimation: Basic Methodologies. In: Li, S., Chu, Z. (eds) Machine Learning for Causal Inference. Springer, Cham. https://doi.org/10.1007/978-3-031-35051-1_3

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