CDT is the academic standard decision theory. Economics, statistics, and philosophy all assume (or, indeed, define) that rational reasoners use causal decision theory to choose between available actions.
Reference?
I’m under the impression that Expected (Von Neumann–Morgenstern) Utility Maximization, aka Evidential Decision Theory is generally considered the ideal theory, while CDT was originally considered as an approximation used to make the computation tractable. In 1981 Gibbard, Harper and Lewis started to argue that CDT was superior to Expected Utility Maximization (which they renamed as EDT), and their ideas were further developed by Pearl, but as far as I know, these theories are not mainstream outside the subfield of causal graphical models founded by Pearl.
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.
I think it is unfortunate that the word “Decision Theory” is used for both VNM and CDT. These are not in the same reference class and are not inconsistent with each other. I think the distinction between CDT and EDT is orthogonal to whether we represent the utilities of the outcomes with a VNM utility function.
CDT says we should make our choice based on the distribution of outcomes if we intervene such that a is chosen. This is in contrast to EDT, which allows you to choose based on the distribution of outcomes in people who were historically observed to choose a. EDT is subject to confounding, therefore quite clearly, Gibbard, Harper and Lewis were correct to argue that CDT is superior to EDT. This is accepted in all academic fields, it is very reasonable to claim that CDT is the standard academic decision theory.
CDT tells you to compare your beliefs about the distribution of Y| do(a) to Y| do(a’) whereas EDT tells you to compare your beliefs about the distribution of Y|a to Y|a’. Note that neither CDT nor EDT specify how to evaluate which distribution of outcomes is better. This is what you need VNM for. You could in principle use VNM for either, but I find it obvious that Von Neumann and Morgenstern were implicitly assuming a Causal Decision Theory.
Reference?
I’m under the impression that Expected (Von Neumann–Morgenstern) Utility Maximization, aka Evidential Decision Theory is generally considered the ideal theory, while CDT was originally considered as an approximation used to make the computation tractable.
In 1981 Gibbard, Harper and Lewis started to argue that CDT was superior to Expected Utility Maximization (which they renamed as EDT), and their ideas were further developed by Pearl, but as far as I know, these theories are not mainstream outside the subfield of causal graphical models founded by Pearl.
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.
I think it is unfortunate that the word “Decision Theory” is used for both VNM and CDT. These are not in the same reference class and are not inconsistent with each other. I think the distinction between CDT and EDT is orthogonal to whether we represent the utilities of the outcomes with a VNM utility function.
CDT says we should make our choice based on the distribution of outcomes if we intervene such that a is chosen. This is in contrast to EDT, which allows you to choose based on the distribution of outcomes in people who were historically observed to choose a. EDT is subject to confounding, therefore quite clearly, Gibbard, Harper and Lewis were correct to argue that CDT is superior to EDT. This is accepted in all academic fields, it is very reasonable to claim that CDT is the standard academic decision theory.
CDT tells you to compare your beliefs about the distribution of Y| do(a) to Y| do(a’) whereas EDT tells you to compare your beliefs about the distribution of Y|a to Y|a’. Note that neither CDT nor EDT specify how to evaluate which distribution of outcomes is better. This is what you need VNM for. You could in principle use VNM for either, but I find it obvious that Von Neumann and Morgenstern were implicitly assuming a Causal Decision Theory.