We’ve now come to one of the most vital aspects of this theory – how can we learn causal models? Learning models is often an exceptionally computationally intensive process, so getting this right is crucial. We now develop some mathematical results which guarantee bounds on our learning. We’ll start by discussing the current state of this field in relation to causal inference and reinforcement learning.
- Causal Reinforcement Learning
- Preliminaries for CRL
- CRL Task 1: Generalised Policy Learning
- CRL Task 2: Interventions – When and Where?
- CRL Task 3: Counterfactual Decision Making
- CRL Task 4: Generalisability and Robustness
- Task 5: Learning Causal Models
- (Coming soon) Task 6: Causal Imitation Learning
- (Coming soon) Wrapping Up: Where To From Here?
Learning Causal Models
Perhaps one of the most computationally difficult processes in the field of causal inference is that of learning underlying causal structure by algorithmically identifying cause-effect relationships. In recent years there has been a surge of interest in learning such relationships in the fields of machine learning and artificial intelligence, though it has been relatively prevalent in the social sciences for many years now (e.g.…