Summary of Benchmarking Estimators For Natural Experiments: a Novel Dataset and a Doubly Robust Algorithm, by R. Teal Witter and Christopher Musco
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
by R. Teal Witter, Christopher Musco
First submitted to arxiv on: 6 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 The paper introduces a novel natural experiment dataset from an early childhood literacy nonprofit, aiming to estimate the effect of treatments. Despite applying over 20 established estimators, inconsistent results are found when evaluating the nonprofit’s efficacy. To address this, a benchmark is created using synthetic outcomes, designed with domain experts’ guidance. The benchmark explores performance under varying conditions like sample size and propensity score accuracy. Results show that doubly robust treatment effect estimators generally outperform others by orders of magnitude. A closed-form expression for the variance of doubly robust estimators is derived, motivating a new estimator design using a novel loss function. The dataset, benchmark, and new estimator are released in a Python package, allowing for easy extension with new datasets and estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to figure out if a treatment works from natural experiments where treatments were already decided. They make a new dataset from an early childhood literacy group that shows different results when using 20+ existing methods. To fix this, they create a test to see which method is best. The test uses fake outcomes and makes it hard for the methods to work well in real-world situations. Results show that some simple methods are much better than others. The paper also explains why these methods work so well and releases the data and test in a package that can be used with other datasets and methods. |
Keywords
* Artificial intelligence * Loss function
