Robust Synthetic Control
Authors: Muhammad Amjad, Devavrat Shah, Dennis Shen
JMLR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments, using both synthetic and real-world datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method. |
| Researcher Affiliation | Academia | Muhammad Amjad EMAIL Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139, USA; Devavrat Shah EMAIL Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139, USA; Dennis Shen EMAIL Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139, USA. All affiliations point to Massachusetts Institute of Technology, an academic institution. |
| Pseudocode | Yes | Algorithm 1 Robust synthetic control; Algorithm 2 Bayesian robust synthetic control |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conduct two sets of experiments: (a) on existing case studies from real world datasets referenced in Abadie et al. (2010, 2011); Abadie and Gardeazabal (2003), and (b) on synthetically generated data. For Basque Country: We only use as data the per-capita GDP (outcome variable) of 17 Spanish regions from 1955-1997. Referenced Abadie and Gardeazabal (2003). For California Anti-tobacco Legislation: we use the annual per-capita cigarette consumption at the state-level for all 50 states in the United States, as well as the District of Columbia, from 1970-2015. Referenced Abadie et al. (2010). |
| Dataset Splits | Yes | Without loss of generality, let the first unit represent the treatment unit exposed to the intervention of interest at time t = T0 + 1. The remaining donor units, 2 i N, are unaffected by the intervention for the entire time period [T] = {1, . . . , T}. Let T0 as the number of pre-intervention periods with 1 T0 < T, rendering T T0 as the length of the post-intervention stage. For synthetic simulations: N = 100, T = 2000, while assuming the treatment was performed at t = 1600. |
| Hardware Specification | No | The paper mentions "Spark (through alternative least squares) and Tensor-Flow come with built-in SVD implementations" and "computational infrastructure", but no specific GPU/CPU models, memory details, or other hardware specifications are provided. |
| Software Dependencies | No | The paper mentions "Spark (through alternative least squares) and Tensor-Flow" as tools that can perform SVD, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The algorithm utilizes two hyperparameters: (1) a thresholding hyperparameter µ 0... and (2) a regularization hyperameter η 0. We will employ three different learning procedures as described in the robust synthetic control algorithm: (1) linear regression (η = 0), (2) ridge regression (η > 0), and (3) LASSO (ζ > 0). In order to choose an appropriate choice of the prior parameter α, we first use forward-chaining for the ridge regression setting to find the optimal regularization hyperparameter η. ... we choose α = η/ˆσ2 where η is the value obtained via forward chaining. |