I am trying to figure out the link between 2-time series vectors.
for example :
X = temperature variation for one year;
Y = strain measurement for one year;
I am new to machine learning. I cannot figure out which unsupervised algorithm to use for identifying possible causality and understanding the link between the 2 vectors? Can I use autoencoders?
In general, complex machine learning methods are not at the core of causality and often not necessary. Most of the time the tricky part is in getting models to estimate the correct quantity. For a primer on causality, this is an accessible resource.
Autoencoders are typically interesting because they can learn lower-dimensional representations of complex data. This is rarely a practical problem in causality, and certainly not for two variables.
To tackle a causal problem, first you need to specify your question: Are you interested in the strength of a causal effect between the variables? Do you know which one is the cause, and which one the effect? Then you will need to think about what data you need to answer the question. Do you have observational and interventional (e.g. randomized control trial) data? Is it a time series? Then you need to express you causal effect, e.g. the average treatment effect, and only then can you use machine learning to estimate it.
