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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Authors: Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish identification for all N 2p parameters despite unobserved confounding. We propose an estimation procedure, Synthetic Combinations, and establish finite-sample consistency under precise conditions on the observation pattern. We formally establish finite-sample consistency of Synthetic Combinations in an observational setting. A key technical challenge in our proofs is analyzing how the error induced in the first step of Synthetic Combinations percolates through to the second step. |
| Researcher Affiliation | Collaboration | Abhineet Agarwal Department of Statistics University of California, Berkeley EMAIL Anish Agarwal Department of IEOR Columbia University EMAIL Suhas Vijaykumar Amazon, Core AI EMAIL. Work done while post-doc at Amazon, Core AI . Work done while at MIT. |
| Pseudocode | No | Section 5 'The Synthetic Combinations Estimator' describes a 'two-step procedure' in prose, but it does not present the steps in a formal 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper does not mention the use of any specific public or open datasets for training, as it focuses on theoretical analysis rather than empirical evaluation. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits, as it is a theoretical work and does not involve empirical experiments. |
| Hardware Specification | No | The paper does not specify any hardware used, as it is a theoretical work and does not report on empirical experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or version numbers, as it is a theoretical work and does not involve software implementation details for experiments. |
| Experiment Setup | No | The paper does not describe any experimental setup details, hyperparameters, or training configurations, as it is a theoretical work and does not involve empirical experiments. |