Summary of Sources Of Gain: Decomposing Performance in Conditional Average Dose Response Estimation, by Christopher Bockel-rickermann et al.
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
by Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke
First submitted to arxiv on: 12 Jun 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 This paper investigates conditional average dose responses (CADR) estimation, a crucial problem that requires modeling complex relationships between covariates, interventions, doses, and outcomes. The machine learning community has developed tailored CADR estimators to tackle specific challenges, evaluated on semi-synthetic benchmark datasets. However, using popular benchmarks without further analysis is insufficient for judging model performance due to multiple challenges entailing distinct impacts. To address this, the authors propose a novel decomposition scheme to disentangle the contributions of five components affecting CADR estimator performance. The scheme is applied to eight CADR estimators on four widely-used benchmark datasets, resulting in nearly 1,500 individual experiments. The findings reveal that most established benchmarks are challenging for reasons different from their creators’ claims, with confounding not being an issue in any considered dataset. The authors discuss the implications and present directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to estimate conditional average dose responses (CADR) correctly. It’s a tricky problem because it involves understanding complex relationships between different things. Some machine learning experts have developed special methods to solve this problem, but they use fake data sets to test their ideas. However, just using these fake data sets isn’t enough to know if the method is good or not. So, the authors came up with a new way to break down what makes each method work well or poorly. They used this idea on many different methods and data sets, doing almost 1,500 separate tests. What they found was that most of these popular data sets are actually really hard for reasons that nobody expected. This means that people need to rethink how they test their ideas in the future. |
Keywords
» Artificial intelligence » Machine learning
