Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Counterfactual Fairness with Partially Known Causal Graph
Authors: Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on both simulated and real-world datasets demonstrate the effectiveness of our method. In this section, we illustrate our approach on a simulated and a real-world dataset by evaluating the prediction performance and fairness of our approach. |
| Researcher Affiliation | Academia | Aoqi Zuo The University of Melbourne EMAIL Susan Wei The University of Melbourne EMAIL Tongliang Liu The University of Sydney EMAIL Bo Han Hong Kong Baptist University EMAIL Kun Zhang Carnegie Mellon University & MBZUAI EMAIL Mingming Gong The University of Melbourne EMAIL |
| Pseudocode | Yes | Algorithm 1 Finding the critical set of S with respect to T in an MPDAG ... Algorithm 2 Identify the type of ancestral relation of S with respect to T in an MPDAG |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The UCI Student Performance Data Set [10] regarding students performance in Mathematics is used in this experiment. |
| Dataset Splits | No | The proportion of training and test data is splitted as 0.8 0.2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. The self-assessment section indicates N/A for compute resources. |
| Software Dependencies | No | The paper mentions tools like "GES structure learning algorithm [5]" and "TETRAD [45]" but does not specify their version numbers or other ancillary software with specific versions. |
| Experiment Setup | Yes | The synthetic data is generated from linear structural equation models according to a random DAG. ...The sensitive attribute can have two or three values, drawn from a Binomial([0,1]) or Multinomial([0,1,2]) distribution separately. The weight, βij, of each directed edges Xi Xj in the generated DAG, is drawn from a Uniform([ 2, 0.5] < [0.5, 2]) distribution. The data are generated according to the following linear structural equation model: ...where 1, ..., n are independent N(0, 1.5). Then we generate one sample with size 1000 for each DAG. ...fitting a linear regression model |