Representation Learning for Treatment Effect Estimation from Observational Data

By Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang. 2018

348 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hanwang Zhang
2. Kaihua Tang
3. Yulei Niu
4. Liuyi Yao
5. Xiansheng Hua
6. Yaliang Li
7. Zhixuan Chu
8. James Jordon

Citations in the corpus

• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “…setting (Johansson et al., 2016; Shalit et al., 2017; Alaa & van der Schaar, 2017; Li & Fu, 2017; Yoon et al., 2018; Alaa & van der Schaar, 2018; Yao et al., 2018) and only a few methods address the longitudinal setting (Xu et al., 2016; Roy et al., 2016; Soleimani et al., 2017; Schulam &…”
• Counterfactual VQA: A Cause-Effect Look at Language Bias (2020, background): “The machine learning community has also made efforts in treatment effect estimation [39, 40, 41] and real-world applications [42, 43, 44, 28, 45, 46, 47].”
• Appendix for “Learning Overlapping Representations for the Estimation of Individualized Treatment Effects” (2020, background): “In DKLITE, the negative log marginal likelihood L (Θ t ) can be rewritten as, where the second equality is achieved by [1,8,14,18,19].”
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, background): “, 2010; Athey and Imbens, 2016; Wager and Athey, 2018; Kallus, 2017) or building balancing representations between patients receiving the different treatments (Johansson et al., 2016; Shalit et al., 2017; Li and Fu, 2017; Yao et al., 2018).”
• From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, unknown): “The causal inference methods that are most appropriate in this case are CMGP, NSGP, Counterfactual Regression, and Similarity Preserved Individual Treatment Effect.”
• A Survey on Causal Inference (2020, methodology): “roup take part in the job training while in the control group are not. The outcome is employment status. Twins. Twins dataset is first introduced in [81] and is adopt by various observational studies [81, 149, 152]. Twins dataset is constructed on the data of twins birth in the USA between 1989-1991”