Summary of Smoke and Mirrors in Causal Downstream Tasks, by Riccardo Cadei et al.
Smoke and Mirrors in Causal Downstream Tasks
by Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 explores the potential of machine learning and AI to transform scientific discovery by enabling accurate predictions for various phenomena. The authors focus on the task of treatment effect estimation, which is crucial for understanding causality in high-dimensional data from Randomized Controlled Trials (RCTs). Despite its simplicity, many common choices in the literature may lead to biased estimates. To test these considerations, the authors created ISTAnt, a real-world benchmark for causal inference downstream tasks on high-dimensional observations. They compared 480 models fine-tuned from state-of-the-art visual backbones and found that sampling and modeling choices significantly affect the accuracy of the causal estimate. The results suggest that future benchmarks should consider real scientific questions, especially causal ones, and highlight guidelines for representation learning methods to answer such questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning and AI to help scientists make accurate predictions and discoveries. It looks at a specific problem called “treatment effect estimation” which is important for understanding what happens when you do something in an experiment. The authors found that many common ways of doing this can actually lead to mistakes. To test their ideas, they created a special dataset called ISTAnt which is based on real-world data from ants! They tested 480 different models and found that the way you choose your model affects how good it is at making predictions. The paper suggests that we should make sure our benchmarks (ways of testing) are realistic and consider what scientists actually want to know. |
Keywords
* Artificial intelligence * Inference * Machine learning * Representation learning
