
## Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Kim, Ameet Talwalkar, Hoda Heidari, Steven Wu
Keywords: 
NeurIPS/2022/Proceedings/54971 - Bayesian Persuasion for Algorithmic Recourse.pdf
Project URL: 

### 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?_

[10]

The authors do not provide their implementation although this is stated to be available in the supplementary material but this is not found.

### 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?_

[3]

(1/1)

The authors use HELOC dataset and provide a citation. the dataset is described and high level statistics are given. More details are given in appendix H/I but no detailed statistics. No direct link.

### 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?_

[4]

The authors detail the hyperparameter values in appendix H and I informally. No overview or acquisition specified.

### 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 results are presented in section 5. The metrics are utility (defined in the introduction). Results are presented over variuous parameter settings. Data splits seem to be not applicable.

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

[7]

Requries expertise on algorithmic recourse.
