Summary of Estimating Causal Effects with Double Machine Learning — a Method Evaluation, by Jonathan Fuhr et al.
Estimating Causal Effects with Double Machine Learning – A Method Evaluation
by Jonathan Fuhr, Philipp Berens, Dominik Papies
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM); 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 The proposed paper reviews and empirically evaluates the “double/debiased machine learning” (DML) framework, a prominent method for estimating causal effects with observational data. By comparing its performance on simulated data to traditional statistical methods, the authors demonstrate that DML’s flexibility in adjusting for nonlinear confounding relationships improves estimation accuracy. They also apply DML to real-world data, estimating the effect of air pollution on housing prices and finding larger estimates compared to less flexible methods. The paper provides actionable recommendations for practical application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how machine learning can help us better understand cause-and-effect relationships from observational data. It looks at a specific method called “double/debiased machine learning” (DML) that uses machine learning to relax some assumptions needed for estimating causal effects. The authors test DML’s performance on made-up data and compare it to older statistical methods, then apply it to real-world data about how air pollution affects housing prices. They find that DML gives more accurate estimates than other methods. |
Keywords
» Artificial intelligence » Machine learning
