Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
Authors: Mehrdad Ghadiri, David Arbour, Tung Mai, Cameron Musco, Anup B. Rao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compliment the above theoretical bounds with experiments using both synthetic and real data, focusing on the full observation ATE and ITE estimation problems. |
| Researcher Affiliation | Collaboration | Mehrdad Ghadiri MIT EMAIL David Arbour Adobe Research EMAIL Tung Mai Adobe Research EMAIL Cameron Musco UMass Amherst EMAIL Anup B. Rao Adobe Research EMAIL |
| Pseudocode | Yes | Algorithm 1: ATE estimation with leverage score sampling and cross adjustment, Algorithm 2: (Ridge) leverage score sampling based regression adjustment, Algorithm 3: ITE estimation with random vector regression and leverage score sampling, Algorithm 4: Regression Adjusted Horvitz-Thompson Estimation, Algorithm 5: Non-Uniform Gram-Schmidt Walk |
| Open Source Code | No | The paper does not provide explicit statements about the release of its source code or links to a code repository for its methodology. |
| Open Datasets | Yes | 1. Boston Dataset [17]: This is a dataset of housing prices in the Boston area, consisting of 506 samples and 13 features. 2. IHDP Dataset [16, 11]: Derived from the characteristics of children and their mothers, this dataset comprises 747 samples and 25 features. 3. Twins Dataset [3]: This dataset is constructed based on the characteristics and mortality rates of twin births in the US. |
| Dataset Splits | No | The paper discusses the datasets used but does not provide specific details on training, validation, and test splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the generation of synthetic datasets and the number of trials for estimation, but it does not specify concrete hyperparameter values or detailed system-level training settings. |