Causal Strategic Inference in Networked Microfinance Economies
Authors: Mohammad T Irfan, Luis E. Ortiz
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study is based on the microfinance data from Bangladesh and Bolivia, which we use to first learn our models. We show that causal strategic inference can assist policy-makers by evaluating the outcomes of various types of interventions, such as removing a loss-making bank from the market, imposing an interest rate cap, and subsidizing banks. |
| Researcher Affiliation | Academia | Mohammad T. Irfan Department of Computer Science Bowdoin College Brunswick, ME 04011 mirfan@bowdoin.edu Luis E. Ortiz Department of Computer Science Stony Brook University Stony Brook, NY 11794 leortiz@cs.stonybrook.edu |
| Pseudocode | Yes | Algorithm 1 Outline of Equilibrium Computation |
| Open Source Code | No | The paper does not provide any specific links to open-source code for the methodology described. |
| Open Datasets | No | The paper mentions obtaining data from ASOFIN and the Central Bank of Bolivia, providing general website links for these organizations, and also data from PKSF, but does not provide direct links to downloadable datasets, specific repository names, or formal citations with author and year for public datasets. |
| Dataset Splits | No | The paper mentions 'training and test errors' in the context of noise models and bootstrapping, but does not specify exact train/validation/test dataset split percentages, absolute sample counts, or reference predefined standard splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models) used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab s large-scale optimization package' and its 'interior-point algorithm' but does not specify version numbers for Matlab or the optimization package. |
| Experiment Setup | No | The paper mentions choosing a value for the diversification parameter 'λ' but does not provide its specific numerical value or other concrete hyperparameters, optimizer settings, or detailed training configurations. |