Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Authors: Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments ( 7) on synthetic and semi-synthetic loan approval data, show the need for probabilistic approaches to achieve algorithmic recourse in practice, as point estimates of the underlying true SCM often propose invalid recommendations or achieve recourse only at higher cost. Importantly, our results also show that subpopulation-based recourse is the right approach to adopt when assumptions such as additive noise do not hold. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2Max Planck ETH Center for Learning Systems, Zürich, Switzerland 3Department of Engineering, University of Cambridge, United Kingdom 4Department of Computer Science, Saarland University, Saarbrücken, Germany |
| Pseudocode | No | The paper describes the gradient-based procedure and other methods in textual form (Section 6) but does not include a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | A user-friendly implementation of all methods that only requires specification of the causal graph and a training set is available at https://github.com/amirhk/recourse. |
| Open Datasets | Yes | We also test our methods on a larger semi-synthetic SCM inspired by the German Credit UCI dataset [34]. |
| Dataset Splits | No | The paper refers to using "synthetic and semi-synthetic loan approval data" and the "German Credit UCI dataset," and mentions a "training set" in relation to code availability, but it does not specify explicit percentages or counts for training, validation, or test splits. |
| Hardware Specification | No | The paper does not specify any details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using Gaussian Processes (GPs) and Conditional Variational Autoencoders (CVAEs), and approaches like stochastic gradient descent, but it does not provide specific version numbers for any software libraries, frameworks, or dependencies. |
| Experiment Setup | Yes | We show average performance ± 1 standard deviation for Nruns = 100, NMC-samples = 100, and γLCB = 2.5. |