Summary of Sample Size Planning For Conditional Counterfactual Mean Estimation with a K-armed Randomized Experiment, by Gabriel Ruiz
Sample size planning for conditional counterfactual mean estimation with a K-armed randomized experiment
by Gabriel Ruiz
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a method for determining the sufficient sample size needed to estimate conditional counterfactual expectations in specific subgroups. The subgroups are defined by feature space partitioning algorithms such as binning users with similar predictive scores or policy trees. By specifying the inference target, minimum confidence level, and maximum margin of error, the authors transform the original goal into a simultaneous inference problem. This allows them to determine the recommended sample size needed to offset increased estimation errors. The paper also explores how to invert this question to find the feasible number of treatment arms or partition complexity given a fixed sample size budget. The authors evaluate their results on a large publicly-available randomized experiment test dataset using policy trees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us figure out how many people we need to study in order to get accurate answers about specific groups of people. These groups can be defined based on similar characteristics or by learning patterns from data. By setting clear goals and requirements, the authors find a way to balance the amount of data needed with the risk of making mistakes. This is important because it helps us make better decisions when working with limited resources. |
Keywords
* Artificial intelligence * Inference
