Consider we want to compare I{I1,I2,I3...In} (a set of manipulable interventions) on outcome Y.  I didn't affect Y directly but through X.  See casual diagram below.

I->X->Y

For example, I are different diets, Y is life expectancy.  X is lowering cholesterol levels. 

The ideal intervention will be I has no effect on Y but affect  X deterministically, its resultant effect on Y will be Q. However, this is ideal which is not feasible right now. So Q can be considered a "theoretical limit"
 

Example of Q as a limit on pending interventions

Q  has it's benefits to policymakers 
Consider a casual diagram I → X → Y, there is no link from I to Y

The overall effect of I on Y can be written in this formula.
E(Y |do(I)) = sum（P(x|do(I))E[Y |do(x)]）
SO if X->Y is 0 ,then I->Y is zero.

For example, 
X is smoking, y is cancer, I is increasing cigarette taxes. “if careful scientific investigations reveal that smoking has no effect on cancer, we can comfortably conclude that increasing cigarette taxes will not decrease cancer rates and that it is futile for schools to invest resources in anti-smoking educational programs.

Practical Benefit of  Q 


Assuming an observational study guided by model M, we identify and evaluate the Q with the expression Q = E[Y |do(x)] = q(x), where q(x) is a function of x computed from the joint distribution of observed variables in the model. 

One can use this information in three ways:
1. Q represents a limit on pending interventions
2. Q tells us about the effects of feasible interventions
3. Q serves as an auxiliary mathematical operation in the derivation of causal effects of manipulable variables.


University of Michigan - Ann Arbor

In conditional probability, when we want to find the likelihood of an occurence given we **know** an *intervention* occured and the value of that intervention, we use the do() operator. 

`do()` operator

Pearl, Judea. “On the Interpretation of Do(x).” Technical Report - UCLA, 13 Feb. 2019.

https://ftp.cs.ucla.edu/pub/stat_ser/r486.pdf


On the Interpretation of do(x)

The following are different ways the do() operator can be used
- P(A | do(B), C)
    Intervening on B, seeing C
    
- P(A | do(B), do(C))
    Multiple interventions
    
- P(A | do(P(B) = P’))
    A change on the distribution of B (not only a point
    mass distribution)

Different uses of do() operator


Pearl (1995) uses expression of the form Q = E[Y |do(X = x)] to denote the causal effect of X on Y .  Where do(x) is a minimally invasive intervention that sets the value of X to x and leaves other relationships unchanged.

However, when X is non-manipulable for experimenters (for example, gender/race are non-manipulable), critics raise concern about the interpretation of the casual effect Q. 


Question of do(x) in Structural Causal Modeling (SCM) 

The Semantics of Q 

When X is non-manipulable, although direct tests of Q(x) are infeasible, indirect tests are available.We can use back-door adjustment or front-door adjustment to test Q.

For example, if a set of variable z satisfies the back door criterion relative to (X,Y),  Q(x) is identifiable and is given by the formula: $$ P(y|do(x))=\sum_{z} P(y|x,z)P(z)$$. 
Equating the adjustment formulae generated by each of those sets provides a test for M, and hence a test for Q.

Fig.1 shows a front-door model where I is manipulable, X is non-manipulable, and U is an unobserved confounder. X satisfies the front door criterion relative to (I,Y). Randomizing I yields an estimate of P(y|do(I)), which should be equal to the estimand of P(y|do(I)) obtained through the front-door formula. The causal effect of I on Y is identifiable and is given by the formula:  $$Q(x)=\sum_{i} P(y|x, I = i)P(I = i)$$


Testing do(x) Claims

Suppose we have a given patient avoids the flue (A) given the flu shot (FS). 
- $P(A|do(FS))$
    - The probability that a typical patient would avoid the flu if the patient got the shot without choice.
- $ P(A|FS) $
    - The probability a patient avoided the flu given they were observed to take the flue shot. This probability asks the observed frequency of patients who avoided the flu when they *volunteered* to get a flu shot

Conditional probability with do() versus without

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