Summary of Learning Decision Policies with Instrumental Variables Through Double Machine Learning, by Daqian Shao et al.
Learning Decision Policies with Instrumental Variables through Double Machine Learning
by Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 instrumental variable (IV) regression, called DML-IV, is introduced to learn causal relationships between confounded action, outcome, and context variables. The method addresses bias issues in two-stage IV regressions by utilizing a non-linear learning objective derived from the double/debiased machine learning (DML) framework. This algorithm achieves strong convergence rates and suboptimality guarantees comparable to those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on benchmarks and learns high-performing policies, even in the presence of instruments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from data uses something called instrumental variable (IV) regression. This helps us figure out how things are related when there’s extra information that can confuse us. A common problem is when we use a two-step approach and it doesn’t work well. The solution is called DML-IV, which reduces the mistakes in the second step. It does this by using a special learning goal and a framework to make sure it works correctly. This new method performs better than other methods on tests and can even learn from situations with extra information. |
Keywords
» Artificial intelligence » Machine learning » Regression
