Statistical Inference and A/B Testing for First-Price Pacing Equilibria
Authors: Luofeng Liao, Christian Kroer
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulations verify our central limit theorems, and empirical coverage rates for our confidence intervals agree with our theory. |
| Researcher Affiliation | Academia | 1IEOR, Columbia University. Correspondence to: Luofeng Liao <ll3530@columbia.edu>. |
| Pseudocode | Yes | Algorithm 1 A/B test effect of a new feature on revenue |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for simulations ( |
| Dataset Splits | No | The paper describes statistical simulations to verify theorems, not traditional machine learning training/validation/test splits of a pre-existing dataset. No specific dataset splits are mentioned for validation purposes. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We clearly see that (i) if β i < 1 then the finite sample distribution is close to a normal distribution, and (ii) if β i = 1 (or very close to 1, such as β14,21 in the uniform value plots, β20,23 in exponential), the finite sample distribution puts most of the probability mass at 1. For cases where β i is close to 1, we need to futher increase number of items to observe normality. |