The context is this - I'm considering doing a part time PhD in statistical learning and today I've met up with a prospective supervisor who suggested that I think about causality in machine learning as (in his words) a lot of people are doing deep learning/neural network stuff and less so in time series learning and causality inference. Causality inference is less developed in machine learning and it's an area I could contribute to.
So I've spent some time googling and thinking and I came away with these elements that I would ideally like to see in a learning algorithm.
- Can handle multi-dimensional time series data
- Online learning
- Time varying relationships
- Can use regularisation
- Allows causality inference (actually, this may overlap with regularisation in some way to prevent overfitting)
This to me sounded like a state-space/dynamic model of some sort. As an example, I could extend the Kalman filter in some way to include the above. Any idea on more specific algorithms I should have a look at?