The naming convention is a bit different here, because we're actually able to go after the conditional total effect directly. That is, we can define a loss function whose minimizer defines the conditional mean of the difference between Qbar_a and Qbar_a_star with respect to the joint distribution of M1 and M2 given C and A. We can then define a submodel through this conditional mean difference (which is exactly the conditional total effect) and target this quantity directly.
1 | target_conditional_total_effect(Qbarbar, gn, Qbar, Y, A, a, a_star, ...)
|
Qbarbar |
Iterated mean estimates |
gn |
Power users may wish to pass in their own properly formatted list of the
propensity score so that
nuisance parameters can be fitted outside of |
Qbar |
Outcome regression estimates |
Y |
A vector of continuous or binary outcomes. |
A |
A vector of binary treatment assignment (assumed to be equal to 0 or 1). |
a |
The label for the treatment. The effects estimates returned pertain
to estimation of interventional effects of |
a_star |
The label for the treatment. The effects estimates returned pertain
to estimation of interventional effects of |
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