Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
By Ahmed M. Alaa, Mihaela van der Schaar. 2018
151 citations recorded by the source corpus.
Subfields: Causal Inference & Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Causal Inference & Discovery
- Yoshua Bengio
- Anirudh Goyal
- Ioana Bica
- Mihaela van der Schaar
- Stefan Bauer
- Ahmed M. Alaa
- Francesco Locatello
- Nan Rosemary Ke
Citations in the corpus
- Toward Causal Representation Learning (2021, background): “While it may be useful to automate certain decisions, an understanding of causality may be necessary to recommend treatment options that are personalized and reliable [3], [6], [31], [164], [201], [224], [242], [273].”
- Towards Causal Representation Learning (2021, background): “While it may be useful to automate certain decisions, an understanding of causality may be necessary to recommend treatment options that are personalized and reliable [201, 242, 224, 273, 6, 3, 30, 165].”
- Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “…data in the static 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…”
- From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, background): “22 The theoretical guidelines provided by Alaa and van der Schaar 22 and validation through synthetic data enables us to narrow down the choice of suitable causal inference methods for the observational data available.”
- 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].”