3ab6be46e1d6b21d59a3c3a0b9d0f6ef-AuthorFeedback.pdf

R1: "I am not entirely convinced that an amortized explanation model is a reasonable thing. R2: "I would appreciate some clarification about what is gained by learning ˆ A and not just reporting Ω directly." We thank R1, R2 and R3 for their insightful feedback. R1: "(The objective) does not attribute importance to features that change how the model goes wrong (...)" R1: "Why is the rank-based method necessary? R2: "Additionally, can the authors clarify what is being averaged in the definition of the causal objective? R2: "If the goal is to determine what might happen to our predictions if we change a particular feature slightly Our goal is not to estimate what would happen if a particular feature's value changed, but to provide a causal explanation R2: "Some additional clarity on why the authors are using a KL discrepancy is merited. R3: "Masking one by one; this is essentially equivalent to assuming that feature contributions are additive." We do not define a feature's importance as its additive contribution to the model output, but as it's marginal reduction This subtle change in definition allows us to efficiently compute feature importance one by one. R3: "Replacing a masked value by a point-wise estimation can be very bad, especially when the classifiers output Why would the average value (or, even worse, zero) be meaningful?" We will clarify this point in the next revision. R3: "It would also be interesting to compare the proposed method with causal inference technique for SEMs." Recent work [29] has explored the use of SEMs for model attribution in deep learning. CXPlain can explain any machine-learning model, and (ii) attribution time was considerably slower than CXPlain. R3: "It seems to me that the chosen performance measure may correlate much more with the Granger-causal loss