Strategic Classification is Causal Modeling in Disguise
Authors: John Miller, Smitha Milli, Moritz Hardt
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. |
| Researcher Affiliation | Academia | John Miller 1 Smitha Milli 1 Moritz Hardt 1 1Department of Computer Science, University of California, Berkeley, Berkeley, California, USA. Correspondence to: John Miller <miller john@berkeley.edu>. |
| Pseudocode | No | The paper contains no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments with datasets. It refers to theoretical constructs like 'joint distribution PX,Y' but no concrete, publicly available dataset is mentioned or cited for experimental use. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, therefore, it does not specify training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |