Summary of Conformal Counterfactual Inference Under Hidden Confounding, by Zonghao Chen et al.
Conformal Counterfactual Inference under Hidden Confounding
by Zonghao Chen, Ruocheng Guo, Jean-François Ton, Yang Liu
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach called wTCP-DR is proposed, which provides confidence intervals for counterfactual outcomes with marginal convergence guarantees. This addresses limitations in existing methods that rely on strong ignorability or require unidentifiable bounds. The wTCP-DR method uses transductive weighted conformal prediction and requires access to a fraction of interventional data from randomized controlled trials to account for covariate shifts. Theoretical results demonstrate the conditions under which wTCP-DR is advantageous over naive methods using only interventional data. This approach can be used to construct intervals for individual treatment effects (ITEs). The method is evaluated on synthetic and real-world data, including recommendation systems, showing superior performance in terms of coverage and efficiency compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict outcomes with uncertainty is developed. This helps people make decisions by considering different possibilities. Existing methods for doing this have limitations. The new method, called wTCP-DR, uses a combination of ideas from previous work and requires some data from controlled experiments. It’s tested on fake and real-world data and performs better than other approaches in making predictions. |
