Summary of Contrastive Representations Of High-dimensional, Structured Treatments, by Oriol Corcoll Andreu et al.
Contrastive representations of high-dimensional, structured treatments
by Oriol Corcoll Andreu, Athanasios Vlontzos, Michael O’Riordan, Ciaran M. Gilligan-Lee
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 The novel contrastive approach proposed in this paper addresses the challenge of estimating causal effects when treatments are structured, high-dimensional objects such as text, video, or audio. Traditional methods assume binary- or continuous-valued treatments, but these assumptions do not hold for many real-world applications. The authors show that using shared structure across treatments blindly can lead to biased estimates and instead propose a representation learning approach that identifies underlying causal factors and discards non-causally relevant ones. This method is proven to provide unbiased estimates of the causal effect, which is demonstrated on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves an important problem in decision making by figuring out how to measure the effects of things like text or video when those things are used as treatments. Usually, we think about treatments being simple things like a pill or a treatment plan, but in reality, many important treatments are more complex objects that can be high-dimensional and structured, like text or audio. The authors show that if we try to use the same approach for all of these different types of treatments, it can actually lead to bad results. Instead, they propose a new way of learning about these treatments that helps us identify what’s really causing an effect and ignore things that aren’t important. This approach is shown to work well on both fake and real data. |
Keywords
» Artificial intelligence » Representation learning
