Answering Complex Causal Queries With the Maximum Causal Set Effect
Authors: Zachary Markovich
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments", "We first consider the performance of this estimation procedure using synthetic data.", "Figure 2 visualizes the results of this analysis.", "Our second application focuses on the role of democratic political institutions in reducing the likelihood of civil war onset. |
| Researcher Affiliation | Academia | Zachary Markovich Massachussetts Institute of Technology Cambridge, MA, 02139 zmarko@mit.edu |
| Pseudocode | No | The paper describes its estimation procedure in prose under '4.1 Algorithm Overview' and provides a mathematical lemma, but it does not include a clearly labeled pseudocode block or algorithm steps formatted like code. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper mentions using 'the Varieties of Democracy Dataset (V-Dem)' for real-world data experiments, but it does not provide a specific link, DOI, repository, or a formal citation with author names and year for public access to this dataset. |
| Dataset Splits | Yes | Specifically, we begin by assuming that the analyst has randomly split the observations into two equally sized sets, SEst and SProb. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions general techniques and models (e.g., Q-learning, linear regression, PCA, Bayesian regression trees) but does not provide specific software names with version numbers or library dependencies used for implementation. |
| Experiment Setup | Yes | For synthetic data, the paper specifies generation parameters: 'ti N(0, Σ)', 'µi = t iβ where β is a length K vector composed of i.i.d draws from the standard normal distribution', 'Yi = µi + ϵi where ϵi N(0, 1)', and simulation parameters 'K = 2, 10, and 50; ρ = 0, .5 and, .9; and values of N between 100 and 1,000'. For real-world data, it states using 'a linear model with fixed effects for the country and year for both ˆP(T = T Max q T = T Min q ) and ˆτ(T , T )'. |