Are causal decision theorists trying to outsmart conditional probabilities?

byCaspar422y16th May 201710 comments


Presumably, this has been discussed somewhere in the past, but I wonder to which extent causal decision theorists (and many other non-evidential decision theorists, too) are trying to make better predictions than (what they think to be) their own conditional probabilities.


To state this question more clearly, let’s look at the generic Newcomb-like problem with two actions a1 and a2 (e.g., one-boxing and two-boxing, cooperating or defecting, not smoking or smoking) and two states s1 and s2 (specifying, e.g., whether there is money in both boxes, whether the other agent cooperates, whether one has cancer). The Newcomb-ness is the result of two properties:

  • No matter the state, it is better to take action a2, i.e. u(a2,s1)>u(a1,s1) and u(a2,s2)>u(a1,s2). (There are also problems without dominance where CDT and EDT nonetheless disagree. For simplicity I will assume dominance, here.)

  • The action cannot causally affect the state, but somehow taking a1 gives us evidence that we’re in the preferable state s1. That is, P(s1|a1)>P(s1|a2) and u(a1,s1)>u(a2,s2).

Then, if the latter two differences are large enough, it may be that

E[u|a1] > E[u|a2].


P(s1|a1) * u(s1,a1) + P(s2|a1) * u(s2,a1) > P(s1|a2) * u(s1,a2) + P(s2|a2) * u(s2,a2),

despite the dominance.


Now, my question is: After having taken one of the two actions, say a1, but before having observed the state, do causal decision theorists really assign the probability P(s1|a1) (specified in the problem description) to being in state s1?


I used to think that this was the case. E.g., the way I learned about Newcomb’s problem is that causal decision theorists understand that, once they have said the words “both boxes for me, please”, they assign very low probability to getting the million. So, if there were a period between saying those words and receiving the payoff, they would bet at odds that reveal that they assign a low probability (namely P(s1,a2)) to money being under both boxes.


But now I think that some of the disagreement might implicitly be based on a belief that the conditional probabilities stated in the problem description are wrong, i.e. that you shouldn’t bet on them.


The first data point was the discussion of CDT in Pearl’s Causality. In sections 1.3.1 and 4.1.1 he emphasizes that he thinks his do-calculus is the correct way of predicting what happens upon taking some actions. (Note that in non-Newcomb-like situations, P(s|do(a)) and P(s|a) yield the same result, see ch. 3.2.2 of Pearl’s Causality.)


The second data point is that the smoking intuition in smoking lesion-type problems may often be based on the intuition that the conditional probabilities get it wrong. (This point is also inspired by Pearl’s discussion, but also by the discussion of an FB post by Johannes Treutlein. Also see the paragraph starting with “Then the above formula for deciding whether to pet the cat suggests...” in the computer scientist intro to logical decision theory on Arbital.)


Let’s take a specific version of the smoking lesion as an example. Some have argued that an evidential decision theorist shouldn’t go to the doctor because people who go to the doctor are more likely to be sick. If a1 denotes staying at home (or, rather, going anywhere but a doctor) and s1 denotes being healthy, then, so the argument goes, P(s1|a1) > P(s1|a2). I believe that in all practically relevant versions of this problem this difference in probabilities disappears once we take into account all the evidence we already have. This is known as the tickle defense. A version of it that I agree with is given in section 4.3 of Arif Ahmed’s Evidence, Decision and Causality. Anyway, let’s assume that the tickle defense somehow doesn’t apply, such that even if taking into account our entire knowledge base K, P(s1|a1,K) > P(s1|a2,K).


I think the reason why many people think one should go to the doctor might be that while asserting P(s1|a1,K) > P(s1|a2,K), they don’t upshift the probability of being sick when they sit in the waiting room. That is, when offered a bet in the waiting room, they wouldn’t accept exactly the betting odds that P(s1|a1,K) and P(s1|a2,K) suggest they should accept.


Maybe what is going on here is that people have some intuitive knowledge that they don’t propagate into their stated conditional probability distribution. E.g., their stated probability distribution may represent observed frequencies among people who make their decision without thinking about CDT vs. EDT. However, intuitively they realize that the correlation in the data doesn’t hold up in this naive way.


This would also explain why people are more open to EDT’s recommendation in cases where the causal structure is analogous to that in the smoking lesion, but tickle defenses (or, more generally, ways in which a stated probability distribution could differ from the real/intuitive one) don’t apply, e.g. the psychopath button, betting on the past, or the coin flip creation problem.


I’d be interested in your opinions. I also wonder whether this has already been discussed elsewhere.


Discussions with Johannes Treutlein informed my view on this topic.