Summary of Do Contemporary Causal Inference Models Capture Real-world Heterogeneity? Findings From a Large-scale Benchmark, by Haining Yu and Yizhou Sun
Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
by Haining Yu, Yizhou Sun
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This study presents a comprehensive benchmarking exercise evaluating Conditional Average Treatment Effect (CATE) estimation algorithms, also known as CATE models. The authors run 16 modern CATE models on 12 datasets and generate 43,200 variants using diverse observational sampling strategies. Surprisingly, the results show that 62% of CATE estimates have a higher Mean Squared Error (MSE) than a trivial zero-effect predictor, rendering them ineffective. Furthermore, in datasets with at least one useful CATE estimate, 80% still have higher MSE than a constant-effect model. The study also finds that Orthogonality-based models outperform other models only 30% of the time, despite widespread optimism about their performance. These findings highlight significant challenges in current CATE models and emphasize the need for broader evaluation and methodological improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well machines can learn to make predictions based on what might happen if something changes. They tested 16 different ways of doing this, called CATE models, on 12 different datasets. What they found was that most of these models don’t work very well and are not much better than just guessing. Only a few of the models did better than others, but even those didn’t do amazingly well. This study shows that we need to come up with new ways of making predictions that are more accurate. |
Keywords
» Artificial intelligence » Mse
