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 Effect Estimation with Mixed Latent Confounders and Post-treatment Variables
Authors: Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both simulated and real-world datasets demonstrate significantly improved robustness of Ci VAE. |
| Researcher Affiliation | Collaboration | Yaochen Zhu1, Jing Ma2, Liang Wu3, Qi Guo3, Liangjie Hong3, Jundong Li1 1University of Virginia, 2Case Western Reserve University, 3Linked In Inc. EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper describes the methodology in detail within Section 4 'METHODOLOGY' but does not include a distinct pseudocode block or algorithm listing. |
| Open Source Code | Yes | Code available at https://github.com/yaochenzhu/CiVAE. |
| Open Datasets | No | The paper uses 'Simulated Datasets' whose generative processes are described, and 'Real-world Datasets' from LinkedIn, which were built by the authors from collected job data. No public access links, repositories, or explicit statements of public availability are provided for these datasets. |
| Dataset Splits | No | The paper describes the generative processes for simulated datasets and the collection of real-world datasets, but it does not specify any training/test/validation splits in terms of percentages, absolute counts, or references to standard predefined splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions) used in the experiments. |
| Experiment Setup | Yes | For Latent Mediator, γ is set as [ 1, 1, 1], θ is set as [1, 1, 1], and τ is set as 2, which results in an ATE = 1. For the Latent Correlator dataset, we set the same γ and θ as the Latent Mediator dataset, where parameters ϕ and τ are set to 1, which results in ATE of 1. Z Gaussian(0, IKZ), X Multi(NNf(Z)), Y Gaussian(w Z, 1), where KZ = 8, and represents the element-wise product operator, respectively. We then treat the first KC = 5 dimensions of Z as the latent confounders C and the remaining KM = KZ KC dimensions as the latent mediators M. After learning NNf and w according to Eq. (11), we draw latent confounders C Gaussian(0, I), latent mediators M = T γ, and set the outcome Y = w [C||M] + τ T, where the true ATE can be calculated as sum(γ w KM:) + τ. |