
## Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann Gaebler, Ravi Shroff, Sharad Goel
Keywords: 
Award: Outstanding Paper
ICML/2022/Proceedings/579 - Causal Conceptions of Fairness and their Consequences.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[2]

The authors provide a link to their implementation in the acknowledgements (https://github.com/stanford-policylab/causal-fairness). In the readme the authors state the abstract of the work. There are no installation instructions. The code has good descriptive comments. Structure is simple.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

The authors use simulated data based on figure 1, for which they provide the code. More details are given in appendix C including parameter values. 

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

The authors use algorithm 1 for their calculation/definition regarding path-sepcific counterfactuals. Only input data, no parameters.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The metric used in figure 2 is task specific. Data split not applicable. No aggregation/variation.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[3]

Requires expertise on fairness
