Counterfactual Fairness with Partially Known Causal Graph
Authors: Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 azuo@student.unimelb.edu.au Susan Wei The University of Melbourne susan.wei@unimelb.edu.au Tongliang Liu The University of Sydney tongliang.liu@sydney.edu.au Bo Han Hong Kong Baptist University bhanml@comp.hkbu.edu.hk Kun Zhang Carnegie Mellon University & MBZUAI kunz1@cmu.edu Mingming Gong The University of Melbourne mingming.gong@unimelb.edu.au |
| 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 |