
## Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill
Keywords: 
JMLR/2023/Proceedings/210670 - Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators.pdf
Project URL: https://github.com/ssomanch/DEE

### 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?_

[1]

The authors provide a link to their implementation on the JMLR website and footnote 8 (https://github.com/ssomanch/DEE). In the readme they provide installation instructions, how to run simulations, how to download and set up data, how to run, how to prdouce figures and statistics and acknowledgements. Code has good comments.

### 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]

(3/3)

The authors present results on 2 synthetic datasets and the Rural Roads dataset (citation provided, link in implementation). The dataset is described in 6.3.1 and visualised in figure 6. Simulations are described in 6.1 and 6.2. and generator code is provided with variables.

### 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?_

[3]

In algorithm 1 they state two parameters t and k, algoirthm 2 K_min, Kmax and Z. The values of k, K_max and K_min are given in appendix A to 200 for computational efficieny, but why specifically 200 is not motivated. A few times k is also set to for example 400. The value of t is set to 30 / 40 in the experiments. Acquisition not clear.

### 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 metrics are MSE / log likelihood and presented as mean and 95% CI over 50 replications. The metrics of table 1 are explained in the caption. Data split not applicable. 

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

[8]

Requries expertise on gaussian processes and regression discontinuity.
