Causal forests are an example that extends random forests, but I imagine a lot of the value in current pipelines would be to use causality as regulariser. This could be a parameter that controls the weight of established causal links, or it could be as a scaffold; e.g. a first 'causal pass' is used to establish constraints (monotonicity, conditional variable selection, reject changes that result in predictions inconsistent with the initial causal model when there is a strong causal model etc).
RL is likely more promising. If agents could be made to search for causality in an environment these relationships could be made much harder to unlearn which would then enable more efficient exploration & incremental learning. Framed this way causality guides attention, limits the search space and locks in learning.
I've got some quarantine reading/experiments to try! :)
As an aside, once while reading through Wasserman's "All of Statistics", he somehow hypnotized me into seeing the title of Ch.16 as "Casual Inference", so anyone who knows me knows that I can't help making a dad-joke about casual inference when the topic comes up.
"One of his systems, which is still in beta, can help scientists determine whether they have sufficient data to answer a causal question. Richard McElreath, an anthropologist at the Max Planck Institute for Evolutionary Anthropology, is using the software to guide research into why humans go through menopause (we are the only apes that do).
The hypothesis is that the decline of fertility in older women benefited early human societies because women who put more effort into caring for grandchildren ultimately had more descendants. But what evidence might exist today to support the claim that children do better with grandparents around? Anthropologists can’t just compare the educational or medical outcomes of children who have lived with grandparents and those who haven’t. There are what statisticians call confounding factors: grandmothers might be likelier to live with grandchildren who need the most help. Bareinboim’s software can help McElreath discern which studies about kids who grew up with their grandparents are least riddled with confounding factors and could be valuable in answering his causal query. “It’s a huge step forward,” McElreath says."
... looks "marginal" if you ask me. But I can't think of a better beta-tester than McElreath for this.
I wonder what the career perspectives that brings? I want to do no statistics, only programming
What’s your PhD work on?
I investigated adjustment sets and instrumental variables.