For a more rigorous explanation, here's the relevant section from MacDermott et al., "Characterising Decision Theories with Mechanised Causal Graphs":
But in the Twin Prisoner’s Dilemma, one might interpret the policy node in two different ways, and the interpretation will affect the causal structure. We could interpret intervening on your policy ˜D as changing the physical result of the compilation of your source code, such that an intervention will only affect your decision D, and not that of your twin T . Under this physical notion of causality, we get fig. 3a, where there is a common cause S explaining the correlation between the agent’s policy and its twin’s.
But on the other hand, if we think of intervening on your policy as changing the way your source code compiles in all cases, then intervening on it will affect your opponent’s policy, which is compiled from the same code. In this case, we get the structure shown in fig. 3b, where an intervention on my policy would affect my twin’s policy. We can view this as an intervention on an abstract "logical" variable rather than an ordinary physical variable. We therefore call the resulting model a logical-causal model.
Pearl’s notion of causality is the physical one, but Pearl-style graphs have also been used in the decision theory literature to represent logical causality. One purpose of this paper is to show that mechanism variables are a useful addition to any graphical model being used in decision theory.
There's a range of interpretations for any counterfactual. One must open up the "suppose" and ask, "What am I actually being asked to suppose? How might the counterfactual circumstance have come to be?" We can accordingly do surgery on the causal graph in different places, depending on how far back from the event of interest we intervene.
To make X counterfactually have some value x, we might, in terms of causal graph surgery, consider do(X=x). Or we might intervene on some predecessors of X, and use do(Y=y) and do(Z=z), choosing values which cause X to take on the value x, but which may have additional effects. Or we could intervene further back than that, and create even more side-effects. We might discover that we are considering a counterfactual that makes no sense — for example, phosphorus matches that do not burn, yet human life continues.
In Newcomb's Problem, the two-boxing argument intervenes on both the decision of the person faced with the problem, and Omega's decision to fill the other box or not, as if there were hidden mechanisms that were to pre-empt both decisions in each of the four possible ways they might be made. (This obviously contradicts one of the hypotheses of the problem, which is that Omega is always right.) The one-boxing argument intervenes on the choice of policy that produces the subject's decision, and does not intervene on Omega.
I could call these CDT and FDT respectively, except for the tendency of people to modify their preferred decision theory xDT in response to problems that it gets wrong, and claim to be still using xDT, "properly understood". I just described the one-boxer's argument in causal terms. That does not mean that CDT, "properly understood", is FDT.
ETA: While googling something about counterfactuals, I came across Molinism, according to which God knows all counterfactuals, and in particular knows what the creatures that he created would do of their own free will in any hypothetical situation. Omega is probably an angel sent by God to test people's rationality. (Epistemic status: jeu d'esprit.)
Regarding (2), I interpret Soares' point as follows: there's a "CDT intuition" and an "FDT intuition" of how to evaluate counterfactuals. Let's just take the Bomb problem as an example.
The CDT intuition deals with which action has the best causal consequences at one given point in time. In Bomb, you can either Left-box or Right-box. There's a bomb in Left, so Left-boxing causes you to die painfully. Right-boxing costs only $100, so right-boxing wins.
The FDT intuition deals with which decision is the best outcome of your decision procedure. Your decision procedure is a function, and could be implemented more than once. In Bomb, it's implemented both in your head and in the predictor's head (she executed it to predict what you would do). Your decision to Left-box or to Right-box therefore happens twice - and, since your decision procedure is a function, it's necessarily the same on both events - and you have to look at the causal consequences of both events. Left-boxing causes the predictor to not put a bomb in Left and you to not lose $100, while Right-boxing causes the predictor to put a bomb in Left and you to lose $100. Left-boxing wins.
P.S. A natural thing to respond here is something like: "But you already see a Bomb in Left, so the FDT intuition makes no sense!" But note that, since the predictor simulates you in order to make her prediction, you don't actually know whether you are the "real you" or the "simulated you", since the simulated you observes the exact same (relevant) things as the real you. (If not, then the simulation would not be accurate and there would be no subjunctive dependence.) So in this intuition, observing the bomb does not actually mean there is a bomb, since you could be in a simulation. In fact, you are, at different points in time, both in the simulation and in the "real" situation, and you have to make a decision that happens in and makes the best of both these situations.
My understanding is that the two contradictory theories are causal decision theory (CDT), which says to choose the action that will cause the best consequences, and evidential decision theory (EDT), which says to choose the action such that the consequences will be best conditional on the action you chose. Newcomb's problem makes causal decision theory look bad but evidential decision theory look good. (CDT two-boxes because your choice seemingly can't cause the prediction, but EDT one-boxes you have more money conditional on one-boxing.) But the smoking lesion problem makes evidential decision theory look bad and causal decision theory look good. (CDT gets to enjoy smoking because it doesn't cause health problems according to the thought-experiment setup, but EDT doesn't because according to the setup, you have health problems conditional on enjoying smoking.)
In this comment thread on the 2021 post “A Defense of Functional Decision Theory”, @So8res wrote:
Following up on my reply that I didn’t know what he was talking about, he recommended some reading:
I followed both of those links, and was not enlightened. The linked FDT paper had this bit:
(This follows a rather technical section, which I have little confidence in having understood correctly.)
Has the dichotomy that @So8res refers to been clearly explained anywhere? If not—can anyone explain it now? The relevant questions are: