Summary of Continuous Treatment Effects with Surrogate Outcomes, by Zhenghao Zeng et al.
Continuous Treatment Effects with Surrogate Outcomes
by Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan Rossi, Ritwik Sinha, Edward H. Kennedy
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel doubly robust method is proposed to estimate continuous treatment effects in real-world causal inference applications where primary outcomes are partially missing, but surrogates (fully observed post-treatment variables related to the outcome) are available. The approach leverages both labeled and unlabeled data to overcome selection bias issues that arise when using fully observed samples alone. Asymptotic normality of the proposed estimator is established, and simulation results demonstrate its appealing empirical performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have found a way to improve how we analyze data where some important information is missing. This can happen when it’s hard or expensive to collect certain details. By using “stand-in” data that is related to what we’re trying to learn, researchers can get more accurate results. This new method combines both the available and missing data to avoid mistakes that can occur when only looking at complete datasets. It works well in simulations and could be useful in many real-world situations. |
Keywords
* Artificial intelligence * Inference
