Summary of Prediction-powered Generalization Of Causal Inferences, by Ilker Demirel et al.
Prediction-powered Generalization of Causal Inferences
by Ilker Demirel, Ahmed Alaa, Anthony Philippakis, David Sontag
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of generalizing causal inferences from randomized controlled trials (RCTs) to target populations where some effect modifiers have a different distribution. Existing work has focused on generalizing without outcome data, but the authors demonstrate that even with covariate data available, the limited size of trials makes generalization statistically infeasible due to the need to estimate complex nuisance functions. To overcome this limitation, they develop algorithms that supplement trial data with a prediction model learned from an additional observational study (OS), making no assumptions about the OS. The authors theoretically and empirically show that their methods facilitate better generalization when the OS is high-quality, while remaining robust in the presence of unmeasured confounding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to use data from clinical trials to make predictions about what will happen in a larger group of people. Right now, it’s not always possible to do this because the trial was only with a small number of people and we don’t know everything about those people that might affect the outcome. The authors propose new ways to combine data from the trial with data from another study (called an observational study) to make more accurate predictions. They show that their methods work well when the additional data is good, but they’re also robust if the extra data isn’t perfect. |
Keywords
» Artificial intelligence » Generalization
