Does the book make any comments about the kind of data you need to be working with?
I have been toying with the notion of looking at a few sets of historical data, and trying to use causal reasoning to establish qualitative causality, even if quantitative is too much to ask.
I’m not sure this will help in your case, but the usual framework for using causality for calculations seems to be that you have a DAG respresenting the causal connections between variables (without probabilities) and statistical data. From this, some things can be calculated that couldn’t be inferred with statistical data alone.
The cause graph can’t usually be inferred from the data. However, some statistical tests could disprove the cause graph. For example, the cause graph might imply that certain statistical variables are independent.
Does the book make any comments about the kind of data you need to be working with?
I have been toying with the notion of looking at a few sets of historical data, and trying to use causal reasoning to establish qualitative causality, even if quantitative is too much to ask.
I’m not sure this will help in your case, but the usual framework for using causality for calculations seems to be that you have a DAG respresenting the causal connections between variables (without probabilities) and statistical data. From this, some things can be calculated that couldn’t be inferred with statistical data alone.
The cause graph can’t usually be inferred from the data. However, some statistical tests could disprove the cause graph. For example, the cause graph might imply that certain statistical variables are independent.