Concept

Using Functional Constraints

We know, given the nature of the problem that Y=H(XB)Y = H \vee (X \land B) This can be reframed as a more concrete counterfactual : PS01=P(Y1=1Y0=0)PS_{01} = P(Y_1 = 1 | Y_0 = 0 ) Where Y1Y_1 is the event HBH \land B and Y0Y_0 is the event HH. PS01PS_{01} is the probability that a death is caused by Russian Roulette alone. Now let, PS10=P(Y1=0Y0=1)PS_{10} = P(Y_1 = 0 | Y_0 = 1 ) By definition, since Y1Y_1 contains Y0Y_0, PS10=0PS_{10} = 0 The law of total probability gives us : P(Y1=1)=(1PS10)(P(Y0=1))+PS01(1P(Y0=1))P(Y_1 = 1) = (1 - PS_{10})(P(Y_0 = 1)) + PS_{01}(1 - P(Y_0 = 1)) We can then solve for PS01PS_{01} to get : P(Y1=1)+P(Y0=1)1P(Y0=1)=16\frac{P(Y_1 = 1) + P(Y_0 = 1)}{1 - P(Y_0 = 1)} = \frac{1}{6} Now we use the equation derived from the law of total probability and plug in values for NYC, to get the estimated mortality rate as $20.8%$

Thus using the structural diagram(s) and functional constraints, we've managed to solve this problem using only the two assumptions we began with.

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Updated 2020-04-26

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Data Science

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