Invariant Causal Prediction for Nonlinear Models
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Christina Heinze-Deml, Jonas Peters, Nicolai Meinshausen |
| Journal/Conference Name | ArXiv e-prints |
| Paper Category | Other |
| Paper Abstract | An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction (ICP) [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence. |
| Date of publication | 2017 |
| Code Programming Language | R |
| Comment |
Copyright Researcher 2021