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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causal Strategic Linear Regression
Authors: Yonadav Shavit, Benjamin Edelman, Brian Axelrod
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents responses, in effect performing causal interventions through the decision rules. |
| Researcher Affiliation | Academia | 1Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA 2Stanford Computer Science Department, Palo Alto, CA, USA. Correspondence to: Yonadav Shavit <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Agent Outcome Maximization |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and describes algorithms with respect to abstract distributions ('P (Rd )') and samples ('{x P}n'), but does not specify any concrete dataset names or provide information about their public availability for training purposes. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., training, validation, test percentages or counts) as it focuses on theoretical algorithms rather than empirical evaluations on concrete datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate any experiments. |
| Experiment Setup | No | The paper does not include specific experimental setup details such as hyperparameter values, optimizer settings, or other training configurations, as it presents theoretical algorithms rather than empirical studies. |