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..
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
Authors: Allen Tran, Aurelien Bibaut, Nathan Kallus
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 uses simulated data to evaluate our method against a range of alternatives, as well as exploring robustness to real world complications. |
| Researcher Affiliation | Collaboration | 1Netflix Inc., Los Angeles, USA 2Netflix Inc., Los Gatos, USA 3Cornell University, New York, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | 4Code and data for the experiments is available at: https://github.com/allentran/long-term-ate-orl . |
| Open Datasets | No | The paper uses simulated data from a simple MDP and a sepsis simulator (Oberst & Sontag, 2019), but does not provide specific access information (link, DOI, or a formal citation for a publicly available dataset) for the data used for training. The provided GitHub link is for the authors' code and data for the experiments, not a general public dataset. |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models) used to run its experiments. |
| Software Dependencies | No | The paper describes the model architecture and estimation methods but does not provide specific version numbers for software dependencies or libraries (e.g., PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For the Q function, we use a feed-forward neural network parameterized separately for each of treatment and control. Each network consists of two hidden layers with 128 and 64 features respectively with sigmoid activation functions and a linear final layer with no activation function. Additionally, we maintain separate target networks by freezing the parameters of each network for 64 epochs... Table 1 in the Appendix lists the parameter values used in the experiments. |