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Describe the bug
As you describe correctly here, the baseline main effect b* of the randomized trial (Setup B) in Nie and Wager (2020) is the following:
b*(X) = max{Xi1 +Xi2,Xi3,0}+max{Xi4 +Xi5,0}
However, in your implementation, you define b* as follows: b = np.maximum(np.repeat(0.0, n), X[:, 0] + X[:, 1] + X[:, 2]) + np.maximum(np.repeat(0.0, n), X[:, 3] + X[:, 4])
Thus, your implementation of b*(X) is:
max{Xi1 +Xi2 + Xi3,0} + max{Xi4 +Xi5,0} but it should be
max{Xi1 +Xi2 , Xi3,0} + max{Xi4 +Xi5,0}
If that is indeed a bug, I would like to submit a merge request.
The text was updated successfully, but these errors were encountered:
Describe the bug
As you describe correctly here, the baseline main effect b* of the randomized trial (Setup B) in Nie and Wager (2020) is the following:
b*(X) = max{Xi1 +Xi2,Xi3,0}+max{Xi4 +Xi5,0}
However, in your implementation, you define b* as follows:
b = np.maximum(np.repeat(0.0, n), X[:, 0] + X[:, 1] + X[:, 2]) + np.maximum(np.repeat(0.0, n), X[:, 3] + X[:, 4])
Thus, your implementation of b*(X) is:
max{Xi1 +Xi2 + Xi3,0} + max{Xi4 +Xi5,0} but it should be
max{Xi1 +Xi2 , Xi3,0} + max{Xi4 +Xi5,0}
If that is indeed a bug, I would like to submit a merge request.
The text was updated successfully, but these errors were encountered: