Summary of Fast Proxy Experiment Design For Causal Effect Identification, by Sepehr Elahi et al.
Fast Proxy Experiment Design for Causal Effect Identification
by Sepehr Elahi, Sina Akbari, Jalal Etesami, Negar Kiyavash, Patrick Thiran
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 novel approach to estimating causal effects combines elements of observational and experimental studies. The traditional methods have limitations – observational studies can be affected by unmeasured confounding, while direct experiments on the target variable may be too costly or infeasible. A middle ground is proxy experiments, which intervene on variables that are less expensive to manipulate. Researchers demonstrated that designing optimal experiments for causal effect identification is NP-complete and developed a naive algorithm. However, this approach has limitations and requires solving many sub-routines. To address this issue, we provide alternative formulations of the problem that enable more efficient algorithms, as validated by extensive simulations. We also explore the related problem of designing experiments to identify effects through valid adjustments sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal effects are important in many fields, but it’s hard to figure out what causes something to happen. Two common ways to do this are observational studies and direct experiments. However, each has its own problems – observational studies can be tricked by hidden factors, while direct experiments might be too expensive or even impossible. A new approach is to use “proxy” experiments that change things in a way that’s less costly but still gets us close to the real effect. Researchers showed that designing these optimal experiments is really hard and requires solving many smaller problems. To make it easier, we rephrased the problem in different ways that allow for faster solutions. We also looked at how to design experiments that can identify specific effects. |
