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].
Identifiable Causal Inference with Noisy Treatment and No Side Information
Authors: Antti Pöllänen, Pekka Marttinen
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate the method s good performance with unknown measurement error. More broadly, our work extends the range of applications in which reliable causal inference can be conducted. We evaluate our algorithm on a wide variety of synthetic datasets, as well as semi-synthetic data. |
| Researcher Affiliation | Academia | Antti Pöllänen EMAIL Department of Computer Science Aalto University Pekka Marttinen EMAIL Department of Computer Science Aalto University |
| Pseudocode | Yes | Algorithm 1 Generation of synthetic datasets using GPs |
| Open Source Code | Yes | The algorithm was implemented in Py Torch, with code available for replicating the experiments at https://github.com/antti-pollanen/ci_noisy_treatment. |
| Open Datasets | Yes | We also test CEME with semisynthetic data based on a dataset curated by Card (1995) from data from the National Longitudinal Survey of Young Men (NLSYM), conducted between years 1966 and 1981. |
| Dataset Splits | Yes | The different training dataset sizes used are 1000, 4000, and 16000 data points. The test data (used for evaluating the models) consist of 20000 data points. [...] The full data of 2990 points is split into 72% of training data, 8% of validation data (used for learning rate annealing and early stopping) and 20% of test data (used for evaluating the models), all amounts rounded to the nearest integer. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The algorithm was implemented in Py Torch, with code available for replicating the experiments at https://github.com/antti-pollanen/ci_noisy_treatment. (No version specified for PyTorch or any other software dependencies). |
| Experiment Setup | Yes | Further training details are available in Appendix B. The hyperparameter values used are listed in Table 1. They were optimized using a random parameter search. [...] The hyperparameter values used are listed in Table 2. The hyperparameters are shared by all algorithms and were optimized using a random search. |