Summary of Proximal Causal Inference with Text Data, by Jacob M. Chen et al.
Proximal Causal Inference With Text Data
by Jacob M. Chen, Rohit Bhattacharya, Katherine A. Keith
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 approach to mitigating confounding bias in text-based causal methods is proposed, addressing limitations in existing techniques that rely on supervised labels. The method leverages two instances of pre-treatment text data, infers two proxies using zero-shot models, and applies these proxies in the proximal g-formula. Under certain assumptions, this approach satisfies identification conditions, while other methods do not. To mitigate untestable assumptions, an odds ratio falsification heuristic is introduced to flag when downstream effect estimation using inferred proxies is appropriate. The method is evaluated in synthetic and semi-synthetic settings, including real-world clinical notes from MIMIC-III, and produces estimates with low bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to fix a problem with text-based causal methods is found! Sometimes, we can’t get the right labels for our data because it’s hard or expensive. But what if we could use just two pieces of text to create some good guesses about things that might affect the outcome? That’s what this paper does! It shows how to take these guesses and plug them into a special formula to make predictions. The scientists tested their method with real-world medical data and found it worked pretty well! |
Keywords
* Artificial intelligence * Supervised * Zero shot
