Policy Analysis using Synthetic Controls in Continuous-Time
Authors: Alexis Bellot, Mihaela van der Schaar
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we experiment with synthetic data from Lorenz s chaotic dynamical system and discuss 2 studies that have received attention in the public policy literature. Evaluation metric. In all experiments, we report mean and standard deviations of the control estimation error... Performance results, as well as an illustration of control paths is given in Figure 2. Continuous-time synthetic controls outperform every other model considered... |
| Researcher Affiliation | Academia | 1University of Cambridge, UK 2Alan Turing Institute, UK 3University of California Los Angeles, USA. Correspondence to: Alexis Bellot <alexis.b11@hotmail.com>. |
| Pseudocode | No | The paper describes an algorithm in Section 3.2, but it is presented as a textual description of steps rather than structured pseudocode or an algorithm block. |
| Open Source Code | No | 2Our implementation will be made available upon acceptance. |
| Open Datasets | Yes | We focus on one country, Spain. The data consists of yearly current account figures from 1980 to 2010 for Spain as well as 15 other countries outside the Eurozone, as collected by David Hope in (Hope, 2016). We follow the experiment by (Abadie et al., 2010) and use annual state-level panel data for the period 1970-2000, giving us 19 years of pre-intervention cigarette sales data. |
| Dataset Splits | No | The paper states 'Performance is computed on a held-out segment of the data' for evaluation, and mentions pre-treatment and post-treatment periods, but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit validation set usage) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud/cluster specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and frameworks like 'Neural CDEs', 'Neural ODE', and 'cubic spline interpolations', but does not provide specific version numbers for any software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states 'Precise experimental details including neural network architectures, optimization software, implementation details and data sources may be found in the Appendix.', indicating that these details are not provided within the main body of the paper. |