Estimating the Long-Term Effects of Novel Treatments
Authors: Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of our proposed estimation strategy we construct a semi-synthetic dataset that retains qualitative characteristics of data on real-world incentive investments in customers at a major corporation, while preserving confidentiality. The semi-synthetic data set preserves several common patterns that require thoughtful attention. |
| Researcher Affiliation | Industry | Keith Battocchi Microsoft Research Eleanor W. Dillon Microsoft Research Maggie Hei Microsoft Research Greg Lewis Microsoft Research Miruna Oprescu Microsoft Research Vasilis Syrgkanis Microsoft Research |
| Pseudocode | No | The paper lists an 'estimation strategy' with numbered steps in Section 2.2, but it does not present this or any other part of the methodology as formal pseudocode or an algorithm block labeled as such. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper states: 'To evaluate the performance of our proposed estimation strategy we construct a semi-synthetic dataset...' and describes the data generation process in the appendix. However, it does not provide concrete access information (link, DOI, specific citation with author/year, or reference to an established benchmark) for this semi-synthetic dataset or the real-world dataset it was based on. |
| Dataset Splits | No | The paper mentions 'increasing the sample size of each simulation' and 'n=1000, n_periods=4, n_exp=100' but does not specify dataset splits for training, validation, or testing, or a cross-validation setup. The data is semi-synthetic, implying the splits would be part of the simulation parameters, which are not detailed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., specific GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper describes the data generation process and high-level evaluation methods, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific training configurations for the models used. |