The cause-effect inference problem is relatively new in the Machine Learning literature and there are still a lot of open problems to be addressed in order to build a robust tool that can be used by the practitioner to confirm for example if a given treatment has an impact or not on a given disease or to discover if a gene has a regulatory power on an other one. Here are the open problems:

• Relax the Causal Sufficiency Assumption
• Need for Real Datasets of a Big Size
• Biased Assessment Due to Artifacts in Data
• Extension of the Generative Approach for Categorical Variables
• Extension of the Pairwise Settnng for Complete Graph Inference
• Computational Complexity Limitations
• Relax Restrictive Assumptions on Causal Mechanisms