Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks

By Bryan Lim. 2018

198 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Medical & Health AI

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

1. Ioana Bica
2. Mihaela van der Schaar
3. Ahmed M. Alaa
4. James Jordon
5. Bryan Lim
6. Sheng Li
7. Jing Gao
8. Aidong Zhang

In Medical & Health AI

1. Ahmed M. Alaa
2. Mihaela van der Schaar
3. Ioana Bica
4. James Jordon
5. Jing Gao
6. Aidong Zhang
7. Zhixuan Chu
8. Yaliang Li

Citations in the corpus

• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “…to learn individualized treatment responses from longitudinal disease trajectories containing information about patient covariates and treatments assignments over time (Robins, 1986; Robins et al., 2000; Robins & Hernán, 2008; Soleimani et al., 2017; Schulam & Saria, 2017; Lim et al., 2018).”
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “See Appendix B for more more details.”
• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, background): “in retail [5, 10], medicine [26, 42] and economics [6], improvements in existing methods bear much significance for practitioners in many domains.”
• A Survey on Causal Inference (2020, methodology): “github.com/d909b/drnet Network Deconfounder [46] Python https://github.com/rguo12/network-deconfounder-wsdm20 Network Embeddings [138] Python https://github.com/vveitch/causal-network-embeddings RMSN [79] Python https://github.com/vveitch/causal-network-embeddings TMLE [96] R https://github.com/josh”
• Causal machine learning for predicting treatment outcomes (2024, unknown): “To address this issue, ML needs to be embedded in a causal framework.”
• From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, unknown): “Lim et al .”
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, background): “Moreover, another important research direction is to extend this work to the temporal causal inference setting [26–29] and estimate counterfactual outcomes for sequences of continuous interventions.”