Learning Treatment Effects in Panels with General Intervention Patterns
Authors: Vivek Farias, Andrew Li, Tianyi Peng
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments on synthetic and real-world data show a substantial advantage over competing estimators. We show both for synthetic and real data that our estimator provides a material improvement in empirical performance relative to available alternatives |
| Researcher Affiliation | Academia | Vivek F. Farias Sloan School of Management MIT Cambridge, MA 02139 vivekf@mit.edu Andrew A. Li Tepper School of Business Carnegie Mellon University Pittsburgh, PA 15213 aali1@cmu.edu Tianyi Peng Department of Aeronautics and Astronautics MIT Cambridge, MA 02139 tianyi@mit.edu |
| Pseudocode | No | The paper describes the algorithm using mathematical equations (1a) and (1b) and accompanying text, but does not provide a formal pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | The first dataset consists of the annual tobacco consumption per capita for 38 states during 1970-2001, collected from the prominent synthetic control study [2] (the treated unit California is removed). The second dataset consists of weekly sales of 167 products over 147 weeks, collected from a Kaggle competition [41]. This dataset consists of daily sales and promotion information of 571 drug stores over 942 days, collected from Rossmann Store Sales dataset [42]. |
| Dataset Splits | No | The paper states hyperparameters were 'tuned using rank r 5 (estimated via the spectrum of M )' and 'estimated via cross validation' without specifying the exact split percentages, sample counts, or the explicit cross-validation methodology (e.g., k-fold, leave-one-out). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or specific computing clusters) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or specialized packages used in the experiments. |
| Experiment Setup | Yes | The hyperparameters for all algorithms were tuned using rank r 5 (estimated via the spectrum of M ). We fix τ = σδ = M /5 through all experiments. We considered an ensemble of 1,000 instances with m1 Uni[1, n1), m2 = Uni[1, n2) for stagger patterns and m1 Uni[1, 5), m2 = 18 for block patterns. A test set Ωconsisting of 20% of the treated entires is randomly sampled and hidden. |