Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Authors: Muhammad Osama, Dave Zachariah, Thomas B. Schรถn
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The properties of the method are demonstrated on synthetic as well as real data from Germany and the US.In this section, we demonstrate the proposed ROSCE method using simulated data for both continuous and discrete space. We subsequently apply the method to real data. |
| Researcher Affiliation | Academia | 1Division of System and Control, Department of Information Technology, Uppsala University. Correspondence to: Muhammad Osama <EMAIL>, Dave Zachariah <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Link to code: github. |
| Open Datasets | Yes | First, we consider the average household income in Euros at county level in Germany for the year 2012 (INKAR, 2012). ... INKAR. German income, age and unemployment data, 2012. URL http://http://www.inkar.de/.We use number of crimes and number of poor families data on county level from US census of year 2000 (Census Bureau, 2000) ... Census Bureau. USA data, 2000. URL https://www.census.gov/prod/www/decennial.html. |
| Dataset Splits | No | The paper describes generating datasets and using observed real-world data but does not specify training, validation, or testing splits or cross-validation strategies. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as programming language versions or library version numbers. |
| Experiment Setup | Yes | For estimation, we use a basis vector ฯ(s) with Ns = 10. To capture multiple resolutions, we use three levels of supports L1 = 0.2 10, L2 = 0.4 10 and L3 = 0.85 10, cf. (16). 3000 bootstrap iterations for both methods. |