Appendix for “Learning Overlapping Representations for the Estimation of Individualized Treatment Effects”

2020

Citations in the corpus

• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Related Work Previous methods for causal inference mostly focused on the static setting (Hill, 2011; Wager & Athey, 2017; Alaa & van der Schaar, 2017; Shalit et al., 2017; Yoon et al., 2018; Alaa & Schaar, 2018; Zhang et al., 2020; Bica et al., 2020c), and less attention has been given to the time s”
• How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2020, methodology): “Methods based on automated ML (Alaa and van der Schaar 2018; Lee et al. 2019; Zhang et al. 2020) can address both of these fail-ings, offering increasingly accurate subtypings of the COVID-19 disease as more data is gathered, while simultaneously uncovering new risk factors as well as interactions…”
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, methodology): “Most of the methods developed in the causal inference literature focus on learning the counterfactual outcomes of discrete interventions, such as binary or categorical treatments2 [1–9].”