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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Instrumental Variable-based Identification for Causal Effects using Covariate Information
Authors: Yuta Kawakami12131-12138
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,651 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,376 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper deals with the identiļ¬cation problem of causal effects in randomized trials with noncompliance. In this problem, generally, causal effects are not identiļ¬able and thus have been evaluated under some strict assumptions, or through the bounds. Different from existing studies, we propose a novel identiļ¬cation condition of joint probabilities of potential outcomes, which allows us to derive a consistent estimator of the causal effect. Regarding the identiļ¬cation conditions of joint probabilities of potential outcomes, the assumptions of monotonicity (Pearl 2009), independence between potential outcomes (Robins and Richardson 2011), gain equality (Li and Pearl 2019) and speciļ¬c functional relationships between cause and effect (Pearl 2009) have been utilized. In contrast, without such assumptions, the proposed condition enables us to evaluate joint probabilities of potential outcomes using an instrumental variable and a proxy variable of potential outcomes. The result of the present paper extends the range of solvable identiļ¬cation problems in causal inference. |
| Researcher Affiliation | Academia | Yuta Kawakami Department of Mathematics, Physics, Electrical Engineering and Computer Science Graduate School of Engineering Science, Yokohama National University 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501 JAPAN EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code or links to a code repository. |
| Open Datasets | No | This is a theoretical paper focusing on identiļ¬cation conditions and does not describe or use any datasets for training or empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation or other purposes. |
| Hardware Specification | No | No experiments are conducted in this theoretical paper, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | No experiments are conducted in this theoretical paper, and thus no software dependencies are listed. |
| Experiment Setup | No | No experiments are conducted in this theoretical paper, and thus no experimental setup details or hyperparameters are provided. |