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].
A Cross-Moment Approach for Causal Effect Estimation
Authors: Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first evaluate our algorithm on synthetic data and compare it to Di D estimator and as well as the related work in [KP14] which estimates the causal effect in linear SCMs with two proxy variables. Further, we apply our algorithm to a real dataset provided by [CK93]. |
| Researcher Affiliation | Academia | Yaroslav Kivva School of Computer and Communication Sciences EPFL, Lausanne, Switzerland EMAIL; Saber Salehkaleybar School of Computer and Communication Sciences EPFL, Lausanne, Switzerland EMAIL; Negar Kiyavash College of Management of Technology EPFL, Lausanne, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 Cross-Moment algorithm |
| Open Source Code | Yes | The implementation of the algorithm and additional experimental results are provided in https://github.com/ykivva/Cross Moments-Method. |
| Open Datasets | Yes | Further, we apply our algorithm to a real dataset provided by [CK93]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., exact percentages, sample counts, or citations to predefined splits) needed to reproduce the experiment for either synthetic or real datasets. It describes data generation for synthetic data and how the real-world dataset is used in a quasi-experimental design, but not standard data splits for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'The implementation of the algorithm and additional experimental results are provided in https://github.com/ykivva/Cross Moments-Method.', but does not list specific ancillary software components with version numbers (e.g., 'Python 3.8, PyTorch 1.9, and CUDA 11.1'). |
| Experiment Setup | No | The paper describes the data generation setup for synthetic data and the real-world dataset application, but it does not provide specific experimental setup details such as hyperparameter values, model initialization, dropout rates, or training schedules for the Cross-Moment algorithm itself. |