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].
Inferring Causal Directions in Errors-in-Variables Models
Authors: Yulai Zhang, Guiming Luo
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some experiments are included to illustrate our method. |
| Researcher Affiliation | Academia | Yulai Zhang and Guiming Luo School of Software, Tsinghua University Beijing, 100084, China P.R. {zhangyl08@mails, gluo@mail}.tsinghua.edu.cn |
| Pseudocode | Yes | We propose an EM style algorithm in this work to solve (6) and (8) together as follow: Step 1. Select the model order n and the length of the instrumental variable q. Initialize the values of the optimization variables in (6) and (8). Step 2. Solve (6), get the result of θ(t) at tth step. Step 3. Solve (8), and get σ2 ex(t). While |σ2 ex(t)/σ2 ex(t 1) 1| > δ, go back to Step 2. Step 4. Calculate the pdf px(x) using (7) Step 5. Calculate ρxy by (5) Step 6. Exchange x and y, do Step 2 to Step 5 again to calculate ρyx. Step 7. If |ρxy| < |ρyx|, the causal direction is x causes y, and if |ρyx| < |ρxy|, the causal direction is y causes x. |
| Open Source Code | No | No statement regarding the release of source code or a link to a code repository was found. |
| Open Datasets | No | The paper describes generating synthetic data: 'The data is from second order polynomial models. θd is randomly generated between [0,2]. The input data x is generated by a uniform distributed between -0.5 and 1.5. σ2 ex and σ2 ey are equally selected from 0.1 to 0.3.' It does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not specify concrete dataset splits (e.g., percentages or counts) for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers with their exact versions) were mentioned in the paper. |
| Experiment Setup | Yes | The paper describes experimental parameters such as the generation of input data x 'by a uniform distributed between -0.5 and 1.5', noise variances 'σ2 ex and σ2 ey are equally selected from 0.1 to 0.3', and parameters for the EM-style algorithm: 'Select the model order n and the length of the instrumental variable q. Initialize the values of the optimization variables in (6) and (8)... While |σ2 ex(t)/σ2 ex(t 1) 1| > δ'. |