You need to do more reading. CDT is basically what all of statistics, econometrics, etc. standardized on now (admittedly under a different name of ‘potential outcomes’), since at least the 70s. There is no single reference, since it’s a huge area, but start with “Rubin-Neyman causal model.” Many do not agree with Pearl on various points, but almost everyone uses potential outcomes as a starting point, and from there CDT falls right out.
The subfield of causal graphical models started with Wright’s path analysis papers in the 1920s, by the way.
edit: Changed Neyman to Wright, I somehow managed to get them confused :(.
This looks like an approach to model inference given the data, while CDT, in the sense the OP is talking about, is an approach to decision making given the model.
I mean this in the nicest possible way, but please understand that if you try to google for five minutes, you are going to be outputting nonsense on this topic (and indeed lots of topics). Seriously, do some reading: this stuff is not simple.
You need to do more reading. CDT is basically what all of statistics, econometrics, etc. standardized on now (admittedly under a different name of ‘potential outcomes’), since at least the 70s. There is no single reference, since it’s a huge area, but start with “Rubin-Neyman causal model.” Many do not agree with Pearl on various points, but almost everyone uses potential outcomes as a starting point, and from there CDT falls right out.
The subfield of causal graphical models started with Wright’s path analysis papers in the 1920s, by the way.
edit: Changed Neyman to Wright, I somehow managed to get them confused :(.
This looks like an approach to model inference given the data, while CDT, in the sense the OP is talking about, is an approach to decision making given the model.
I mean this in the nicest possible way, but please understand that if you try to google for five minutes, you are going to be outputting nonsense on this topic (and indeed lots of topics). Seriously, do some reading: this stuff is not simple.