Venue: CLeaR 2022 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: Summary: A useful theoretical analysis of causal concepts for explainable AI that could use substantial revisions to the front of the paper
Main review: The introduction distinguishes between explaining the model itself and explaining the real world. These are profoundly different things targets for explanation, and much of the work in XAI has tended to focus on the first. That is, work in XAI aims to answer the question: “Why did the model make conclusion X?” rather than “Why would making conclusion X be justified by the way that the world works?” While this is made fairly clear later in the paper, the beginning of the paper reads as if the authors believe that most work in XAI targets the latter (much more difficult) question. The introduction should be revised to better reflect the later material.

The focus of the paper (“…conceptual analysis of the relevant notions of causal explanations…”) is interesting, though limited. Still, it is a sufficient contribution to warrant acceptance.

The authors claim that “If one is interested in ﬁnding an action that changes an already observed output, then Counterfactual Explanations are called for.” This is not what many researchers mean when they say “counterfactual explanation”.  Rather than focusing on *outputs*, they focus on *inputs* (conditions). A counterfactual explanation says, what output would have been produced by the model given a different conditions.

Strong assumptions, such as assuming perfect knowledge of a causal model M of the target domain, imply some responsibility on the part of the authors to either argue that (1) such an assumption is reasonable in practice (a hard sell, in this case); or (2) plausible violations of the assumption do not invalidate the conclusions of the analysis based on the assumptions. In this paper, we have neither. Greater discussion around this point would improve the paper.

The paper is reasonably critical of current work on XAI (e.g., “…the literature on XAI has failed to take up the most relevant lesson for XAI that the literature on causation has to offer…”). When leveling such criticisms, it is important to accurately characterize the goals of the work being critiqued. In at least some portions, the current paper does not accurately characterize the work. Specifically, it assumes that all work on XAI intends to explain why a given action is expected to achieve a particular outcome (how the world works) rather than to explain why a given model recommended a given action (how the model works). This difference is a fundamental one, and it is one that has been discussed extensively among (at least some) researchers working on XAI.

Of course, a quite plausible question is whether it is possible to produce counterfactual explanations (about the model alone) that will be correctly interpreted. The explanation “Our model for credit scoring would have produced a different outcome if your education level had been higher” appears only slightly different than saying “You would be more worthy of credit if you had more education”. The two statements are miles apart in terms of the knowledge required of the world to make them with confidence, but users of an explanation system might easily confuse them.  Furthermore, one might argue (and the current paper does) that counterfactual explanations of the model alone are really not possible without implicit reference to the world. This argument should be foregrounded rather than remain in its current (somewhat buried) position in the paper.

The discussion at the beginning of Section 5 is an extremely important one for the XAI enterprise, and the paper would be improved if the introduction of the paper did a better job of forecasting this future discussion rather than (as it does now) assuming that most XAI literature is doing far more than what Section 5 calls “the first interpretation”.
review_title: null

final_score: rating: 6: Marginally above acceptance threshold
review_content: Summary: This paper give discussion about sufficient explaination, counterfactual explaination, and actual causation explaination.
Main review: This paper introduce the relations between XAI and causation, and give some clearly and insteresting definination of causation based explaination. I think such discussion contributes to make the action guiding explaination to go one step further. BTW, is it necessary to require the ML model and the causal model agree on each other? Maybe it can be relaxed.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: Summary: This paper introduces and motivates refined notions of causal explanations in deterministic structural causal models. 
Main review: This paper introduces and motivates refined notions of causal explanations in deterministic structural causal models. These are compared with alternative notions of causal explanations and discussed alongside the corresponding notions of causation. It is being argued for why the presented notion of causal explanations is useful beyond the already existing notions. The rather abstract discussion is illuminated with a few examples. Overall a good paper, accept.



# Pros:

* Well written

# Cons:

* unclear what is new in this paper.

* significance for machine learning is mostly limited to this conceptual extension of causal terminoloy


# Originality

This point is my major concern. I find it not obvious to disentangle which parts of the paper are a collection and comparison of notions that have already appeared elsewhere and which parts are new to the paper. In my opinion the paper would benefit from stating this more clearly, for example by including a list of the key novel contributions in the introduction.
As far as I understand, the various notions of causation (weak sufficiency, direct sufficiency, strong sufficiency, counterfactual dependence, actual causation) are taken from the literature (mostly Beckers, 2021) while the notions of causal explanations derived thereof are new to this paper. This, i.e., going from notions of causation to notions of causal explanations, is a logical step to take. Further novel aspects are the proofs that several distinct notions of causation and explanation collapse under the independence assumption as well as the (brief) application to fairness.

# Significance

As showcased by the examples, the proposed notion of sufficient explanation does capture important information about how the actual value of the target variable has been obtained or how the desired value can be obtained. I can see this being relevant for action-guiding and thus think the discussion is relevant from a general point of view. The significance for machine learning is mostly limited to this conceptual extension of causal terminology, while it is not discussed how this new terminology can be implemented concretely to explain the decisions of machine learning models or how to inform machine learning models by causal knowledge. In this sense one might say that the paper is more about causal reasoning in general rather than about XAI, but this is a matter of perspective I suppose. Also, the demonstration of how restricting the independence assumption is as well as the discussion on fairness in section 6.1 appear relevant to XAI.


# Quality

The paper is of good quality. The discussion and comparison of several notions of what causation can be understood to mean in a structural causal model, with fine but important variations, as well as the associated notions of causal explanations appear quite illuminating to me. Moreover, the propositions and theorems answer questions that naturally come up when reading the definitions and discussion. While I did not check the proofs, the statements seem plausible. There are no obvious errors that I can see.

# Clarity

Due to the level of abstraction and the long list of interdependent definitions with fine variations, the paper is in my point of view a tough read. However, I think this is mostly due to the nature of the topic rather than the clarity of writing and exposition. In fact, the paper follows a clear thread and the author takes care to motivate the definitions and to illustrate some of them with examples. That being said, there are a few details in which I think the discussion could be more clear or precise, see the section "Other comments" below.



# Further comments

* First paragraph of section 3: The term "target domain" is somewhat vague, what exactly needs to be known?
* "Technically this means that there are only two kinds of endogenous variables..." in the first paragraph of section 3. Why is this required? Even if I_b depends on I_a and an endogeneous variable V(I_b), Y could still be deterministically determined by \bold{I}, could it not? I suppose you want that Y does not depend on an endogeneous variable which is not in \bold{I}.
* In Definition 3 the statement "I = i --> Y = y" is used as a causal formula. I suppose "\bold{I} = \bold{i} --> Y = y" means that if \bold{I} = \bold{i} then Y = y. However, I do not see how this fits into the general definition of causal formulas in section 2.
* What is the set C in definition 6? Is it C = V \ (X \cup Y)? Or is the statement supposed to hold for all or for one C \subset V \ (X \cup Y)?
* Typo in Theorem 16: There is an additional closing parenthesis in "W_2 = w_2)"
* The fire / burning house example confuses me because I do not see how it makes a case for using actual causation. Do you mean that F = 1 is not actual cause of B = 0 while F = 0 is?
* What does "causes" mean in Definition 25? I suppose this refers to actual causation, right?
review_title: null

Meta-review: This paper draws on the actual causation literature, and defines and compares several concepts of causal explanation that are arguably relevant to explainable AI. The paper is clearly written and technically sound. The particularly interesting and original contributions of this paper include making explicit the action-guiding implications of the various conceptions of causation and explanation, and relating the notion of actual causation (as opposed to counterfactual explanation) to the important issue of fairness.

On the other hand, this paper works with deterministic rather than probabilistic causal models, which in my view is its main limitation. It is unclear whether generalisations to probabilistic causal models would be as straightforward as the author seems to suggest. (One question is whether one could define a probabilistic notion of actual causation as opposed to a notion of the probability of actual causation, and if so, whether they suggest significantly different notions of explanation.)

Overall I agree with the three reviewers that this is a good paper for the conference.
Decision: Accept (Oral)