Summary of Towards Representation Learning For Weighting Problems in Design-based Causal Inference, by Oscar Clivio et al.
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
by Oscar Clivio, Avi Feller, Chris Holmes
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 A reweighting strategy for estimating various causal effects, which minimizes the distance to a target distribution, is explored in this paper. The focus is on design-based weights, which don’t rely on outcome information and are commonly used in prospective cohort studies, survey weighting, and augmented weighting estimators. The authors highlight the importance of representation learning in finding suitable weights, rather than assuming a well-specified representation. They propose an end-to-end estimation procedure that learns a flexible representation while retaining theoretical properties. This approach is shown to be competitive in various causal inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new method for estimating causal effects using design-based reweighting. They show how representation learning can help find the best weights and propose an end-to-end procedure that combines these ideas. The results are promising and could be used in many real-world applications. |
Keywords
» Artificial intelligence » Inference » Representation learning
