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 Strategic Classification: A Tale of Two Shifts
Authors: Guy Horowitz, Nir Rosenfeld
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and semi-synthetic data demonstrate the utility of our approach. ... Finally, we conduct a series of experiments that empirically validate our approach. |
| Researcher Affiliation | Academia | 1Technion Israel Institute of Technology, Haifa, Israel. Correspondence to: Nir Rosenfeld <EMAIL>. |
| Pseudocode | Yes | Pseudocode for our entire procedure is given in Algorithm (1). |
| Open Source Code | Yes | All code is made publicly available and can be found at: https://github.com/guyhorowitz/CSC. |
| Open Datasets | Yes | We now turn to experiments based on real data using two public datasets: (i) spam, used originally in Hardt et al. (2016), and (ii) card fraud, used in Levanon & Rosenfeld (2021). ... The data is publicly available at https://www.kaggle.com/datasets/mlg-ulb/ creditcardfraud. |
| Dataset Splits | Yes | Next, we split the data roughly 60-10-30 into train, validation, and test sets. ... We sampled 5500 balanced samples for the experiment and set 3000 of them (~54%) as training data, 500 (~9%) as validation data, and 2000 (~36%) as test data. |
| Hardware Specification | No | The paper provides details on data processing, feature partition, labeling functions, and training parameters (e.g., learning rates, batch size, epochs), but it does not specify the hardware (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using KDE with a Gaussian kernel and implies Python for the open-sourced code, but it does not provide specific version numbers for software dependencies or libraries (e.g., PyTorch, scikit-learn). |
| Experiment Setup | Yes | For both card fraud and spam experiments, we used the following parameters, which we choose manually: ... 2. f, h learning-rate = 0.01 3. batch size = 64 4. epochs = 100 5. an early stopping mechanism when there are 7 consecutive epochs without accuracy improvement on the validation set 6. sigmoid temperature τ = 4 7. exploration regularization coefficients: in CSERMλ = 0.1 we used λ0 = 0.1 which decays in each round with factor of 0.4. in CSERMλ = 1 we used λ0 = 1 which decays in each round with factor of 0.4. |