Causal Modeling for Fairness In Dynamical Systems
Authors: Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that when environment dynamics are unknown, causal reasoning can help utilize observational data to improve off-policy estimation and learning. |
| Researcher Affiliation | Academia | 1University of Toronto 2Vector Institute. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code at github.com/ecreager/causal-dyna-fair |
| Open Datasets | No | We generate observational data from the SCM in Figure 4a, under a MAXPROF threshold policy. We then consider a new policy πτ with per-group thresholds {τj} as its two parameters. |
| Dataset Splits | No | We search over the space of two-threshold policies (one threshold per group) to find the policy with the highest off-policy estimate of the objective on a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper describes the general experimental setup in terms of the SCMs and policies being evaluated (e.g., MAXPROF vs. EQOPP policies, threshold parameters), but it does not specify concrete hyperparameter values for any learning algorithms (e.g., learning rates, batch sizes), model initialization, or detailed system-level training settings. |