Here is the standard causal graph for Newcomb’s problem (note that this is a graph of the agent’s actual situation, not a graph of related historical data):
Given that graph, my CDT solution is to return the action A with highest sum_payoff { U(payoff) P(payoff | do(A), observations) }. Given that graph (you don’t need a causal graph of course), my EDT solution is to return the action A with highest sum_payoff { U(payoff) P(payoff | A, observations) }.
That’s the easy part. Are you asking me for an algorithm to turn a description of Newcomb’s problem in words into that graph? You probably know better than me how to do that.
Here is the standard causal graph for Newcomb’s problem (note that this is a graph of the agent’s actual situation, not a graph of related historical data):
Given that graph, my CDT solution is to return the action A with highest
sum_payoff { U(payoff) P(payoff | do(A), observations) }. Given that graph (you don’t need a causal graph of course), my EDT solution is to return the action A with highestsum_payoff { U(payoff) P(payoff | A, observations) }.That’s the easy part. Are you asking me for an algorithm to turn a description of Newcomb’s problem in words into that graph? You probably know better than me how to do that.