In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Authors: Amir Feder
Guy Horowitz
Yoav Wald
Roi Reichart
Nir Rosenfeld
Venue: NeurIPS 2022 Accept (2022)
Keywords: Behavioral User Modeling
Bounded Rationality
Robust Prediction
Causal Representation Learning

Abstract: Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content---making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for \emph{how users causally perceive the environment}. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.

In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder Columbia University amir. reder@columbia edu
Guy Horowitz Technion
Yoav Wald Johns Hopkins University
Roi Reichart Technion
Nir Rosenfeld
Technion
Abstract
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content makitt relevancy moving target. which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the enviroument. We model users as boundedly-rations decision makers whose causal belief's are encoded by causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.
Introduction
Across multitude of domains and applications, machine learning has become imperative for guiding human users in many of the decisions they make [44,58, 10]. From recommendation systems and search engines to e-commerce platforms and online marketplaces, learned models regularly used to filter content, rank items, and display select information-all with the primary intent helping users choose items that are relevant them. The predominant approach for learning in these tasks is to train models to accurately predict the relevance of items to users. But since training often carried out on logged historical records, even highly-accuran accurate models remain calibrated the distribution of previously observed data on which they were trained [7,59,56]. Given that in virtually any online platform the distribution of content naturally varies over time and location- dune to trends and fashions, innovation, or forces of supply and semand-modee trained on logged data may fail to correctly predict the choices and preferences of users unseen, future distributions [12,1,18,27,34)
In this paper, we present novel conceptual framework for learning predictive models of user-item relevance that are robust changes in the underlying data distribution. Our approach is built around two key observations: (i) that relevance to users is determined by the way in which users perceive value, and (ii) that this process of value attribution is causal in nature. As example, consider video streaming service in which a user u is trying to determine whether watching a certain movie will be worthwhile. To make this decision, the movie, x, and wystem-generated. personalized relevance score, r (c.g.. "a 92% match!"). How will she integrate these on her belief as to why a particular relevance score is coupled to particular movie. For example, if movie boasts high relevance score, then she might suppose this score given because the system believes the user would like this movie. Another user, however, may reason differently, and
{0. 1). the user has at her disposal a feature description of
informational sources into decision? We argue that this crucially hinges
36th Conference on Neural Information Processing Systems (NeurTPS 2022).

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instead believe that high relevance scores are given because movies are sponsored; if she suspects this be likely scenario, her reasoning should have a stark effect on her choices. In both cases above, perceived values (and the actions that follow) stem from how each user causally interprets the recommendation environment e, and the underlying causal structure determines how belief regarding value changes, or does not, in response to changes in important variables (e.g., in x, or r).
Here, we show how knowledge regarding the causal perceptions of users can be leveraged for pro- viding distributional robustness in learning. A primary concern for robust learning is the reliance of predictions on spurious correlations [3]: here we argue that spuriousness can result from causal perceptions underlying user choice behavior. To see the relation between causal perceptions and spuriousness, assume that in our movies example above, the training data exhibits strong correlation between users' choices of movies, and a 'genre' feature. xg. A predictive model optimized for accuracy will likely exploit this association, and rely on xg for prediction. Now, further assume that what realy drives user satisfaction is 'production quality', x if xx and x, are spuriously correlated in the training data, then once the distribution of genres naturally changes over time, the predictive model can fail: the association between Xg and y, on which predictions rely, may no longer hold.
In essence, our approach casts robust prediction of personalized relevance as a problem of out-of- distribution (OOD) learning, but carefully tailored to settings where data generation is governed by users' causal beliefs and corresponding behavior. There is growing recognition of how a causal understanding of the learning environment can improve cross-domain generalization [3,55]; our key conceptual contribution the observation that, in relevance prediction, users' perceptions are the causal environment. Thus, there is no 'true' causal graph-all all is in the eye of the beholder. To cope with this, we model users as reasoning about decisions through a causal graph [35,47]]thet allowing our approach to anticipate how changes in the data translate to changes in user behavior. Building on learning inferred
this idea, as well as on recent advances in the use of causal modeling for [3,55,54], we show how various levels of knowledge regarding users' causal or assumed-car be utilized for learning distributionally robust predictive models.
To encourage predictions & to be invariant to changes in the recommendation environment e, our approach enforces independence between ÿ and (possibly given y). This achieved through regu- larization, which penalizes predictive models for re- lying on [22]. In general, different graphs require different regularization schemes [54]; in our con- text, this would seem to imply that invariant learn- ing requires precise information regarding each user's causal graph. However, our key observation is that, for user graphs, it suffices to know which of two classes the graph belongs to co-causal or anti- causal- determined by the direction of edges be- tween x, r and y (Fig. la). Thus, correctly deter- mining which regularization apply requires only minimal graph knowledge.
y
X,
y
(a)
(b)
Figure I: Simplified graphs describing users of different: (a) classes: causal or anti-causal, and (b) subclasses: believer or skeptic (here shown for a causal user). Dashed lines indicate possible spuriousness (e.g., via selection).
Nonetheless, more fine-grained information can still be useful. We show the following novel result: if two users generate the same data, but differ in their underlying graph, they will have different optimal out-of-distribution predictive models (despite sharing the same optimal in-distribution model). The reason for this is that, to achieve robustness, regularizing for independence will result in the discarding of different information for each user. Operationally, this means that learning should include different models for each user-type (not doing so implicitly constrains the models to be the same). Here again show that minimal additional information is useful, and focus on subclasses of graphs that differ only in the direction of the edges between x and r, which give rise to two user subclasses: believers and skeptics (Fig. 1b). Nonetheless, our result on differing optimal models applies more broadly, and may be of general interest for causal learning.
We end with a thorough empirical evaluation of our approach ($5), where we explore the benefits of different forms of knowledge regarding users' causal beliefs: whether they are casual or anti-causal, and whether they are believers or skeptics. Our results show that learning in a way that accounts for users' causal perception has significant advantages on out-of -fi-rrribttttiionnn tasks. We also study
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the degree to which imprecise graph knowledge is useful: our results here show that even a rough estimate of a user's class sufficient for improved performance, suggesting that our approach can be effectively applied on the basis of domain knowledge or reasonable prior beliefs. Conversely, our results also imply that not accounting for causal aspects of user beciision-makngg or modelling them wrongly, can result in poor out-of dittof--iitribttoon performance. As systems often also play a role in determining what information is presented to users (e.g., providing r), understanding possible failure modes-and and how to obtain robustness becomes vital.
Broader aims. We aim to promote within machine learning the idea of modeling users as active and autonomous decision-makent with emphasis on capturing realistic aspects of human Our approach crucially hinges on modelling users as decision-maid makers that (i) reason causally, (ii) are boundedly-rational and (iii must cope with uncertainty. As we will show, all three are key to our framework, and operate in unison. Each of the aspects above relates to one of three main pillars on which modern theories of decision-making stand [46], thus blending three different fields golds-discrete choice (economics), causal modeling (statistics), and domain generalization (machine learning).
2 Related Work
Causality and Recommendations. Formal causal inference techniques have been used extensively in many domains, but have only recently been applied to recommendations [29.57,7,59,550 Liang et al. [30] use causal analysis to describe a model of user exposure to items. Some work has also been done understand the causal impact of these systems on behavior by finding natural experiments in observational data [43,50,40], and through simulations [1 1 1,39). Bottou et al. [8] use causally-un mymtiivatddd techniques in the design of deployed learning systems for ad placement to avoid confounding, As most of this literature addresses selection bias and the effect of recommendations on user behavior [7,59,56). there is no work. as far as we know, that models boundedly rational agents interacting with a recommender system. Moreover, we are the first to propose modeling users' (ereepttnttptios about the recommendation generation process using causal graphs.
Bounded Rationality and Subjective Beliefs. The bounded rationality literature focuses on modelling agents that make decisions under uncertainty, without the ability fully process the state of the world, and therefore hold subjective beliefs about the data-generating process. Eyster and Rabin [17] defined cursed beliefs, which capture an agent's failure to realize that his opponents' behavior depends on factors beyond those he is informed of. Building on Esponda and Pouzo [16], who modelled equilibrium belief' under misspecified subjective models, Spiegler [47] used causal graphs to analyze agents that impose subjective causal interpretations on observed correlations. This work lays the foundation upon which we model users here, and has sprouted many interesting extensions [14,15).
Causality and Invariant Learning. Correlational predictive models can be untrustworthy [24], and latch onto spurious correlations, leading to errors in OOD settings [33,20,19], This shortcoming can potentially be addressed by a causal perspective, as knowledge of the causal relationship between observations and labels can be used mitigate predictor reliance on them [9,54]. In our experiments, we learn a representation that is invariant to interventions on the 'environment' e, a special case of an invariant representation [3,28,4]. Learning models which generalize OOD is fruitful area of research with many recent developments and implications of invariant learning depend on the data's 's true causal structure. Specifically, distinct causal structures require distinct regularization schemes to induce invariance.
Recently, Veitch et al. [54] showed that the means
3 Modelling Approach
3.1 Learning Setting
In our setting, data consists of users, items, and choices. Users are described by features E Rd,, and items are described by two types of features: intrinsic item properties, x E Rd (e.g., movie genre, plot synopsis, cast and crew), and information provided by the platform, r e Rd, (e.g.. recommendation score, user reviews). We will sometimes make distinction between features that are available to users, and those that are not; in such cases, we denote unobserved features by X, and with slight abuse of notation, use for the remaining observed features (we assume is always observed). Choices y E (0, 1) indicate whether a user u chose to interact (e.g., click, buy, watch) with certain item (x, r). Tuples (u, x, r., y) are sampled iid from certain unknown, joint distributions, which we define next.
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As we are interested in robustness to distributional change, we follow the general setup of domain generalization [6.26,5,55] in which there is a collection of environments, denoted by a set &, and each environment E E defines a different joint distribution De over (,) We assume there training data available from a subset of K environments, Erain = {e]:...,ek C E, with datasets S = [Uug Xki= Ma== Vx)) mi drawn i.i.d from the corresponding D We denote the pooled training distribution by Dteain = rei
/=1
D and the pooled training data by S = S with m = Σx myx.
Our goal is to learn a robust predictive model p f'u, x, r; 0) := fx((x, r; 0) with parameters 0; the fu notation will be helpful in our discussion of robustness as it emphasizes our focus on individual users. We now turn to define the precise type of robustness that we will be seeking.
Robustness via causal graphs. The type of robustness that we would like our model to satisfy is counterfactual invariance (CI) [54]. Denoting x(e), r(e) as the counterfactual features that would have been observed had the environment been set to e, this is defined as:
Definition 1. A model fu is CI if, e E it holds a.e. that fx(((e'). r(e';; 0) r()))
The challenge in obtaining CI predictors is that at train time we only observe a subset of the envi- ronments, ain C E, while CI requires independence to hold for all environments e E E. To reason formally about the role of e in the data generating process, and hence about the type of distribution shifts under which our model should remain invariant, it is common assume that a causal structure underlies data generation [23,3]. This is often modeled as a (directed) causal graph [35]; robustness then defined as insensitivity of the predictive model to changes (or 'interventions') in the variable e, which can trigger changes in other variables that lie 'downstream' in the graph. To encourage robustness, a common approach is construct learning objective that avoids spurious correlations by enforcing certain conditional independence relations to hold, c.g., via regularization (see $4). The question of which relations are required can be answered by examining the graph and the conditional independencies it encodes (between e, x, r, and f). Unfortunately, inferring the causal graph is in general hard; however, determining the 'correct' learning objective may require only partial informa- tion regarding the graph. We will return to the type of information we require for our purposes, and the precise ways which we use it, in $4.
3.2 Users as decision makers
Focusing on relevance prediction, at the heart of our approach lies the observation that what underlies the generating process of data, and in particular of labels, is the way in which users causally perceive the environment. In this sense, users are the causal mechanism, and their causal perceptions manifest in their decision-making Operationally, we model users as acting on the basis of individualized causal graphs [47] that define how changes in one variable propagate to influence others, and ultimately- determine choice behavior. This allows to anticipate, target, and account for sources of spuriousness.
Rational users. To see how modeling users as causal decision-makens can be helpful, consider first a conventional 'correlative' approach for training f. e.g. by minimizing the loss of a corresponding score function (x, r) = v(u, x, r), and predicting via ÿ = argmax ye10,1) (x,, = 1 (x,, > 0). From the perspective of user modeling, Un can be interpreted as personalized "value function'; ; this complies with classic Expected Utility Theory (EUT) [53], in which users are modeled as rational agents acting to maximize (expected) value, and under full information.' From causal perspective, this approach is equivalent to assuming a graph which all paths from e to y are blocked by x, x,r-h is akin to assuming no spurious pathways, and so handicaps the ability to avoid them.
users. We propose to model users as boundedly- -rational decision-makee under the key assertion that users' decisions take place under inherent uncertainty. Uncertainty plays key role in how we, as humans, decide: our actions follow not only from what we know, but also from how we account for what we don't know. Nonetheless, and despite being central to most modern theories of decision making [25] and despite being a primary reason for why users turn to online informational services like recommendation systems in the first place- eexplicit modeling of user-side uncertainty is currently rare within machine learning [37,2.88]; here we advocate for its use.
Boundedly-
Our modeling approach acknowledges that users know some features are unobserved, and that this influences their actions. Here we demonstrate how this relates robust learning through an illustrative
For simplicity, here and throughout we consider deterministic valuations, although this is not necessary.

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User beliefs:
unobserved by user
Data generation:
correlation via Integration on
Model A:
Model B:
(incorrect)
ignored
(incorrect)
conditianed
no effect on
Pu
change
offected
unaffected
unofffcced
D(x,rle) p. [u[x,r,e]= Σs (y(c.
S (x,T)
g = (x,
perceived value
integrate uncertuinty unobserveds
all user-observed features
system -howwn features
Figure 2: (Left) User beliefs and resulting data generation process of y. The user does observe x, but knows it is missing. To compensate for this uncertainty, the user integrates over x w.r.t. probabilistic beliefs Pae which can depend on e; this forms her perceived value which determines her choice y. Integrating uncertainty can introduce correlation between and E (and hence y) through P.l)))) Note y does not depend on instances of x. (Center) Learning a predictive model f(x, r) using only features observed by the user. Assuming that x can be discarded creates the impression that information cannot flow from to y, implying (wrongly) that naïvely trained f(x. would be robust. In practice, such an f might incorrectly use r to compensate for variation in e. (Right) Learning predictive model xx.x, using all features available to the system. Assuming affects implies (wrongly) that all paths from e to are blocked. Naively training f will likely use variation in explain y; this might improve performance on observed e, but will not generalize to others.
example using a particular behavioral model, though as we will show, our approach applies more broadly. Consider user shopping online for vintage coat, and considering whether to buy a certain coat. The coat's 's description includes several intrinsic properties x (e.g.. the coat's material), as well as certain platform-selecter user wants to make an informed decision, but knows some important information, X, is missing (e.g.. the year in which the coat was manufactured). If she is concerned about buying a modern knockoff (rather than truly vintage coat), how should she act? common approach to extend EUT to support uncertainty by modelling users as integrating subjective beliefs about unobserved variables, Pa (,],,, r, e), into conditional estimate of value, (x rle), over which choices y are made:
information (e.g., stylized photo of a vintage-looking coat). The
(xexx Σ ()p
y (0, rle) > 0}
(I)
Here, (x)xx, e) describes user's (probabilistic) belief regarding the conditional likelihood of each x (is the vintage-looking coat truly from the 60's?), and (x, x, x) describes the item's value to the user given & (if the coat really is from the uncertainty beliefs (,|x, r, e) can be environment-speciff (i.c., the degree of suspicion regarding knockoff's can vary across retailers). In turn, value estimates 0 and choices y can also rely on (note that & can also be interpreted as the conditional expected value, (x rle) = [x r)). Since is a deterministic function of ő (Eq. (1)), for clarity (and when clear from context) we will "skip" 0 and refer to y direct function of x, r, and e.
much is it worth to me?). Importantly, note that
Causal user graphs. One interpretation of Eq. (1) is that users cope with uncertainty by employing causal reasoning [47], this aligning with predominant approach in the cognitive sciences that views humans as acting based on 'mental causal models' [45]. Here we follow [47] and think of users reasoning through personalized user causal graphs, denoted Gg. The structure of G, expresses u's causal beliefs nemiel which variables causally affect others and its factors correspond to the conditional terms (p,, and v,,) in Eq. (1). A key modeling point is that causal perceptions; hence, different users may have different graphs that encode different conditional independencies, these inducing different simplifications of the conditional terms. For example, user that believes movies with a five-star rating (r) are worthwhile regardless of their content (x) would have e, r) reduced to (x, r), since 1l x|r; meanwhile, user who, after reading movie's description (x), is unaffected by its rating (r), would have U, (x, X) instead, since u Il r|x.
can vary in their
3.3 User behavior and spurious correlations
We are now ready to make the connection to learning. Recall that our goal is to learn a predictor f that is unaffected by spurious correlations, and that these can materialize if some mechanism creates
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an association between and we will now see how user behavior can play such a role. Continuing our illustrative example, assume that the beliefs of our boundedly-rational user (who chooses via Eq. (1)) are encoded by the leftmost diagram in Fig. 2 ('User beliefs'). The diagram does not show an edge between e and y. However, and crucially, the user behaves 'as if" there actually was an edge: by accounting for uncertainty via integration, x is effectively 'removed" from the indirect path e-X-y, which results in a direct connection between e and y. The corresponding data-generation process of choices y is illustrated in Fig. 2 ('Data generation"). This supports our main argument: by making decisions, users can generate spurious correlations in the data-here by accounting for uncertainty.
The above has concrete implications on learning. First, it shows how conventional learning approaches can fail. On the one hand, since users observe only x and r, one reasonable approach would be to r; discard X altogether, and train a predictor (x. 0) in hopes of mimicking user choice behavior. This means learning as if there no edge between e and y (Fig. 2, 'Model A'). Under this (incorrect) assumption, for Su to be robust to variation in e, it would suffice to train using conventional approach, e.g., vanilla ERM. But the user knows X exists, and by integrating beliefs, relies on this for producing y importantly in way that does depend e. This makes learning fx r; 0) prone to using x and r to compensate for the constant effect of each train-time environment e on y. By definition, once the environment changes, naively trained fu cannot account for the new (residual) effect of on y.
Conversely, the system may choose to learn using all information that available to it, namely train a predictor fx(x, X, r; O) (Fig. 2, 'Model B¹). This make sense if the goal is in in-distribution (ID) generalization. But for outto--distiiuutionn this creates an illusion that conditioning on x, x, r will block all paths from and y, again making it tempting to (wrongly) conclude that applying ERM would suffice for robustness. However, because the system does observe instances of X (note it still exists in the data generating process), learning can now erroneously use the variation in X to explain y. whereas the true y does not rely on specific instantiations of x. As a result, a naively learned fu will likely overfit to training distributions in Erain and may not generalize well to new environments.
Second, the awareness to how users account for uncertainty suggests a means to combat spuriousness. Eq. (1) shows that y depends on x, and e; hence, since our goal is to discourage the dependence of 9 on e, follows that (i) functionally, fu should not depend on X. but (ii) fu should be learned in a way that controls for (conditional) variation in e. In our example, this manifests in the role of X: at train-time use X (perhaps indirectly) to learn a function that at test-time does not rely on it (c.f. Model B which uses X for both train and test, and Model A which does not use X at all). Since the precise way in which e relates to other variables is determined by the user graph G2 (which determines conditional independencies). knowledge of the graph should be useful in promoting invariance. In the next section we describe what knowledge is needed, and how it can be used for invariant learning.
4 Learning With Causal User Models
Our approach to robust learning is based on regularized risk minimization, where regularization acts to discourage variation in predictions across environments [54,55]. Our learning objective is:
argmin L(f:5)+ + fEF
Sx)
(2)
where Fis the function class, is the average loss w.r.t an empirical loss function (c.g., log-loss), and is a data-dependent regularization term with coefficient A. In our approach, the role of R is to penalize f for violating certain statistical independencies: the question of which independencies should be targeted and hence the precise form that R should have- caree-anhle be answered by the underlying causal graph [54]. Knowing the full user graph (e.g. detailed causal relations between different features within x, such as whether the genre of movie is a cause for its production quality) can certainly help, but relying on this (and at scale) is impractical. Luckily, as we show here, coarse information regarding the graph can be translated into necessary conditions for distributional robustness, and in $5 we will see that these can go a long way towards learning robust models in practice.
Note that by choosing to promote robustness through regularization, our approach becomes agnostic the choice of function class F (although the graph may also be helpful in ?) We also need not
"The careful reader will notice that Fig. 2 reveals how spuriousness can arise, but not yet how it can be handled, Indeed, the latter requires additional structure (which the figure abstracts) that is descrihed in Sec. 4. EE.., g., if we aim learn parameterized predictors on the basis of Eq. (1). graphs can help discard dependencies.

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commit to any specific behavioral choice model. This allows us to abstract away from the particular behavioral mechanism that generates spuriousness (e.g,, integration of x), and consider general relations between e and y (e.g., selection or common cause); we make use of this in our experiments.
Regularization schemes. We focus on two methods for promoting statistical independence: MMD [22], which we present here; and CORAL [52], which we describe in Appendix C (we use both in our experiments). The MMD regularizer applies models f that can be expressed as a predictor h applied to a (learned) representation mapping Φ. i.e., f = hog (note h can be vacuous). MMD works by encouraging the (empirical) distribution of representations for each environment eg to be indistinguishable from all others; this is one way to express the independence test for ÿ and e [54]. In our case, for a single eg. .MM is instantiated as:
M x_2), where & = [Ø(x) x E Sgl. 4_ = (Ø(x) x E S {
(3)
As we show next, the precise way in which MMD is used for regularization depends on the graph.
4.1 User graph classes: causal vs. anti-causal
Following example in Fig. la, consider users of two types: 'causal' user u__y that believes value is an effect of an item's description (i.e., (x u = u_yy) is entailed by the graph x, x-r for each e E), and 'anti-causal' user "-y that believes the item's value causes its description (i.e., Dx(xrr,y I u = u-y) is entailed by x, x. respectivlly). 4 Our next result shows that: (i) d- and y require different regularization schemes; but (ii) the appropriate scheme is fully determined by their type irrespeccteee of any other properties of their graphs. Thus, from a learning perspective, suffices to know which of two classes user belongs to: causal, anti-causal.
Proposition 1. Let f be a CI model and assume y and e are confounded (e.g., e- exists), then:
(I) fu.., must satisfy PDr Su.., (xx1)) P (Iu., (x.1)) Ve,e' E
(2) fu., must satisfy P Su_., (x,
PD [u (x,1) Ve,e' E yE,
On the other hand, Su.., need not necessarily satisfy (2), and Su.., need not necessarily satisfy (1).
If we fail to enforce these constraints during learning, then we will not learn CI classifier. On the other hand, enforcing unnecessary constraints (e.g., requiring both conditions hold for fa., and f restricts our hypothesis class and hence limits performance. The proof follows directly from [54] (under technical assumptions; see Appendix A). The distinction between causal and anti-causal is fundamental in causality [41]; for our purposes, it prescribes the appropriate regularization.
Corollary 1. For any user u, to encourage fa(x, r) to be invariant to changes in e, set:
+ -Kk) R(f:S) ( E. MMD( k. E, E. MMD( () 66) KN Rk
u is causal
(marginal MMD)
(4)
is anti-causal
(conditional MMD)
where 1D k...
(1) includes the subset of examples with user u and label y, respectively.
When learning over multiple users (Eq. (2)), the operational conclusion is that all users of the same class regardless of their specific graphs- sraphl-should should be regularized in the same manner, as in Eq. (4).
4.2 User graph subclasses: inter-feature relations
Consider now two users that are of the same class (i.e., causal or anti-causal) but perceive differently the causal relations between x and r: a believer, ux who believes recommendations follow from the item's ' attributes; and a skeptic, Mx--r who presumes that the system reveals item attributes to match desired recommendation (see Ib). Our main result shows that even if both users share the same objective preferences and hence exhibit the same choice patterns-to be optimally invariant, each user may require her own, independently-traine model (though with the same regularization).
Proposition 2. Let ux-rr be users of the same class (i.e., causal anti-causal) but of different subclass (i.e., believer and skeptic, respectively). Even if there is a single predictor f which is optimal for the pooled distribution D Dirain each user can have a different optimal CI predictor.
'Technically, anti-causal users have r.- and y function of C, but use x.re for consistency.
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Proof is in Appendix A. Prop. 2 can be interpreted as follows: Take some u. and 'counterfactually" invert the edges between and r. In some cases, this will have no effect on u's s behavior under Erain and so any I that is optimal in one case will also he optimal in the other. Nonetheless, for optimality to carry over to other environments different predictors may be needed. This is since each causal structure implies a different interventional distribution, and hence different set of CI predictors: e.g.. in - the y-structure e-x-r suggests that an invariant predictor may depend on r, yet in Gx-r it cannot. If the sets of CI predictors do not intersect, then necessarily there is no single optimal model.
different
The practical awke-away is that even if two different users exhibit similar observed behavioral patterns (e.g., differences in their graphs are not expressed in the data), whether they are skeptics or believers has implications for robust learning; Prop. 2 considers an extreme case. Luckily, for data with mixed subclasses, having multiple training environments enables us to nonetheless learn invariant predictors, inferred or assumed and learning a different
e.g., by partitioning the data by user model for each subclass (with regularization determined by class).
Graph knowledge: inference vs. belief's. Formally, both Prop. and Prop. 2 require that we precisely know each user's s class and subclass, respectively, which amounts to inferring the directionality of subset of edges. In principle, this can be done via experimentation (e.g., using focused interventions such as A/B tests) or from observational data (using simple conditional independence tests, e.g., [48,21]) [42]. While this is certainly easier than inferring the entire graph, orienting edges can still be challenging or expensive. Nonetheless, Prop. can still be practical useful when there is good domain knowledge regarding user classes, either the individual or population level: if the learner has certain prior beliefs regarding users' causal perceptions, Eq. (4) provides guidance as to how to devise the learning objective: what regularization to apply (Prop. 1), and how to partition the data (Prop. 2). Our experiments in Sec. 5, which we present next, are designed under this perspective.
5 Experiments and Results
We present three experiments: two targeting user classes (causal or anti-causal) and using real data, and one targeting user subclasses (believers and skeptics) and using synthetic data. Appendix B includes further details on model architectures, training procedures, and data generation.
5.1 Learning with causal users: text-based beer recommendation
Data. We use RateBeer, a dataset of beer reviews with over 3M entries and spanning 10 years [32]. We use the data to generate beer features x (e.g., popularity, average rating) and r (e.g., textual review embeddings) and user features " (c.g., average rating, word counts). Given a sample (u, x, r), our goal is to predict (binarized) rating y. Here focus on causal users, and so would like labels y to expresses causal user beliefs. The challenge is that our observational data is not necessarily such. To simulate causal user behavior, we rely on the observation that x, r- means "changes in x, r affect y", and for each u create an individualized empirical distribution of 'counterfactual samples (x', r', ,')))' that approximate the entire intervention space (i.e., all counterfactual outcomes y' under possible interventions (x, r) + (x', r')). Training data is then generated by sampling from this space.
We consider each year as an environment e, with each inducing a distribution over x,, We implement spuriousness via selection: Each e entails different fashionable 'tastes' in beer, expressed as different weighting over the possible beer types (e.g., lager, ale, porter). Labels are then made to correlate with tastes in a certain temporal pattern. This serves as a mechanism for spurious correlation.
Results. Fig. 3 compares the performance over time of three training procedures that differ only in the type of regularization applied: causal, anti-causal, and non-causal. Our data includes behavior generated by causal-class users; results demonstrate the clear benefit of using behavioralll-consiste regularization scheme (here, causal). Note the causal approach is not optimal in 2006 and 2008; this is since correlations in e are make these years similar to the training data. However, in the face of significant shifts in taste, other approaches collapse, while the causal approach remains stable.
5.2 Learning with anti-causal users: clothing-style recommendation
Data. We use the fashion product images datasets, which includes includes 44. 4k fashion items described by images, attributes, and text. Here we focus on anti-causal users, and generate data
5https://www. www.we..ggel.oooooo.ooooo. wwt.c///rrraaaagaaaa///ssiiiiiia produccttoprou-tto-o iwtt-iiimagsssssssss

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anth-causal (consistent) (inconsittens) cousal
tost accuraty
test accuracc
60
70
50
40
(consistent)
anti-causal (inconsistent)
2007
year
mean correlation in treining set
Figure 3: RecBeer Results. For each year, models are trained on past data (starting 2002), and predict on the following year. The cansal training scheme. consistent with the user class, outperforms other methods when beer-type fashions (e) changes, Periods with substantial change are highlighted in tan.
Figure 4: RecFashion Results. Environments vary in the correlation between item colors and user choices. The anti-causal regulariza- tion scheme, consistent with the user class, outperforms methods when test-time deviates from train-time correlation (=0.8). When cor- relations flip (< 0.5), other methods crash.
in a way similar to $5.1, but using an anti-causal intervention space. In this experiment we let user choices y E {0, 1} depend on an item's image and color, which be either red or green; in this way, is the item's ss grayscale image, and r its hue (which we control). Here we consider environments that induce varying degrees of spurious correlations between color and user choices, Ply = I[ed)) P(y Olgreen) = Pe- For the test set we use Pe 0.8, and experiment with training data that gradually deviate from this relation, i.c.. having P E [..1,
Results. Fig. 4 shows that consistent regularization (here, anti-causal) outperforms other alternatives whenever correlations deviate from those observed in training. Once correlations flip (< 0.5), both causal and non-causal approaches fail catastrophically; the anti-causal approach remains robust.
Accuracy for the user subclass experiment. Rows show train conditions: with and without regularization, and which are included in the training set. Columns show test conditions: ID/OOD, and user type. Best results for each train condition (rows) are highlighted in bold.
Table 1:
Xsp
X
Accuracy (ID) OOD) skeptic
Reg-
Users @train
believer
process for the user subclass experiment (synthetic). Here, x factorizes into an anti-causal component X and a spurious component Xsp linked with Spuriousness results from selection bias between y yad e.
Figure 5:
skeptic
78.0 / 50.0
89.8 / 75.1
0
believer
78.0/ 50.0
89.8 / 75.1
both
78.0 / 50.0
89.8
skeptic
71.1 / 75.6
74.67 775.5
1>0 0
believer
69.8 / 52.5
88.5 / 85,2
both
70.03 / 64.57
78.88 / 78.13
5.3 Learning with multiple user subclasses
Our final experiment studies learning with users of of the same-class (here, anti-causal) but different subclasses: skeptics believers. Our analysis in $4.2 suggests that each user subclass may have different optimal predictor, here we investigate this empirically on synthetic data.
Data. The data- gettr-generaiig process is as follows (see Fig. 5.2). We use three environments: e,, at train, and e; test, and implement a selection mechanism (dashed line) that causes differences in p(yle;) across e,, Since we focus anti-causal users, features x, are determined by y. We use three binary features: xsp ('spurious"), ), x ('anti-causal")) ), and r. These are designed so that an ʃ which uses xgc alone obtains 0.75 accuracy, but using also x,p improves in-distribution (ID) accuracy slightly to 0.78, and so the optimal ID predictor for both user subclasses of the form f*(x xxp). However, relying on Xsp causes out- notd-f-diisribbttion (OOD) performance to deteriorate considerably;

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thus, robust models should not learn to discard sp The role of r is to distinguish between user subclasses: The skeptic does not need r since, for her. it is fully determined by xsp the optimal invariant predictor is hence (x-=,(Xac) Meanwhile, the believer, due to the v-structure xp e, can benefit in-distribution by using both r and xspi here, the optimal invariant predictor is
fx=-(X00- r).
Results. Table I shows ID and OOD performance for each user subclass (columns), for learning with and without regularization (rows). Since all users are anti-causal, we use anti-causal (i.e., conditional) regularization. We compare learning a separate predictor for each user type (rows "skeptic" and "believer') and learning single predictor over all users jointly ( ('both"). Results show that without regularization (2 = 0), ID performance is good, but the learned predictor fails OOD drastically for skeptic users (note all rows are the same since both user types share the same ID-otimmal f*). In contrast, when regularization is applied (2 > 0). learning an independent predictor for each user subclass performs well OOD (for both subclasses), indicating robustness to changing environments; note that ID performance is also mostly maintained. Meanwhile, learning on the entire dataset (i.e., including both user types) does provide some robustness but suboptimal both ID and OOD.
5.4 Learning with mixed sub-populations
Our previous experiment considered setting in which the learner has exact information regarding each user's sub-type, and so can correctly partition the population in a way that is optimal in regards to Prop However, such precise knowledge may not be available in practice, or may be too costly or difficult to infer. In this section we experiment in a setting where the learner has only coarse information (or general beliefs) about user (sub-)types. Our results suggests that following the practical conclusions of Prop. 2-namely partitioning the population of users based on (estimated) types and learning a different predictive model for each can be beneficial even when based only on a reasonable guess.
Data. We use the setting of Sec.5.3 with a popu- lation of anti-causal users, composed of two sub- populations of skeptics and believers. We then sim- ulate a setting where the learner has imprecise infor- mation about user types by adding noise: we move an a- fractioon of each subpopulation into the other, this creating two mixed sub-populations. with in- creasing levels of 'impurity'. a E [0, 0.25]. Here, a 0 represents perfect information. whereas a 0.25 entails large minority groups (25%).
Performance with A = 30
Separate Datanets
Pooled Dataset
000 Accunacy
0.55
0.74
073-
0.72
Results. Fig. 6 compares OOD performance of the robust model (solid line) to naive model trained on the pooled dataset (dashed line). In line with pre- vious results, for O, the robust model achieves significantly higher accuracy on the test environ- ment. As increases, the robust model preserves its advantage, with accuracy degrading gracefully; for 0.25, the robust model still outperforms the pooled baseline. Thus, our results show that despite having imperfect information regarding user sub-types, learning distinct models for each subpopulation. as Prop. 2 suggests, remains beneficial.
0.00
0.05
0.10
0.15
0.20
0.25
User
Figure 6: Learning with a mixed population of believers and skeptics. Even when the minority group is large (25%). learning in a way that is tailored to the majority group is still beneficial.
6 Discussion
Humans beings perceive the world causally; our paper argues that to cope with a world that changes, learning must take into account how humans believe these changes take effect. We identify one key reason: in making decisions under uncertainty, users can cause spurious correlations to appear in the data. Towards this, we propose to employ tools from invariant causal learning, but in a way that is tailored to how humans make decisions, this drawing on economic models of bounded-rations rounded-rationalityy. Our approach relies on regularization for achieving invariance, with our main point being that how and what to regularize can be derived from users' causal graphs. Although we have argued that even partial graph information can be helpful-even this form knowledge is not straightforward to obtain (notably at test-time), and may require experimentation. Nonetheless, and in hopes of spurring further interest, we view our work as taking one step towards establishing a disciplined causal perspective on the interaction between recommending systems and the eeecisionmmakingg users they aim to serve.
10

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Acknowledgements
This work was supported in part by The Israel Science Foundation (grant 278/22).
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15


Reviews:
[{"review_id": "xpFs33qT7gJ", "reviewer": "Reviewer_EVAJ", "summary": "This paper studies the problem of predicting item relevance to users, in a way that is robust to changes in the data distribution. It is a particular case of tackling the bigger challenge of out of distribution generalization - here different environments are represented by different users' beliefs. The paper gives a strategy for trustworthy relevance prediction which does not require full information of the underlying causal graph. It only requires one to know whether the graph is causal or anti-causal, and for each of these scenarios an appropriate regularization scheme is chosen. ", "questions": "My only question is the same as Point number 1 in weaknesses. ", "limitations": "The authors have mentioned limitations of their method both in the discussion section as well as in the description of their experimental results. ", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is very well-written and the motivation and setup are very clear. I enjoyed reading this work. \n2. The learning algorithm proposed requires minimal knowledge of the causal graph (i.e. whether it corresponds to (a) causal and (b) anti-causal with two possible subclasses - believer or skeptic; this latter sub-distinction is very nice and quite realistic for modeling user behavior). \n3. The connection to economics and user behavior psychology by modeling the response y via an expected utility function is interesting. \n4. This model also accounts for the case in which some intrinsic information about the product (encoded in x) is missing. \n5. The algorithm is clear and the experiments are thorough, accounting for all possible scenarios. \n\n\nWeaknesses:\n\n1. While I intuitively understand it, it is still unclear to me how the MMD and the CORAL regularization methods satisfy the respective necessary conditional independence requirements needed for preventing spurious correlations. Similarly, it is not completely mathematically clear to me why the conditional independencies described in lines 216-217 are satisfied. \n2. Inferring the type of causal and non-causal graph is still non-trivial. The authors do mention some approaches for this in lines 294-295, i.e. A/B testing or conditional independence testing from observational data, but the paper could significantly benefit from a more developed discussion of possible ways to infer the graph in practice. \n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "muwJMxw-2GN", "reviewer": "Reviewer_HSTu", "summary": "The paper considers a recommendation setting where we observe the attributes of users, of items, and aspects of the recommendation itself such as a relevance, an explanation of the relevance, etc. The task is to use this information to predict the user's decision, whether or not they will interact with the item of interest. The decision could be binary, a rating, or something else.\n\nThe problem is to produce a predictor of user decisions that is invariant to changes in the distribution of the observed features (attributes, recommendation aspects, etc.). We expect such invariant predictors to achieve bounded risk when deployed to unseen distributions of data. This paper closely follows work on counterfactual (CF) invariance, a criteria based on counterfactual distributions that captures desirable behavior in predictors. The criteria can be *approximately* satisfied by regularizing predictors (where the regularization crucially depends on the causal model of the features and label).\n\nTo extend the ideas of invariance to user decision predictions, the paper makes a key insight: the decision is affected by the causal model by which a user reasons about features to make a decision. Thus, a key idea in the paper is to parameterize such causal models of user reasoning. After formalizing a notion of CF invariance in the user decisions setting, the paper uses the idea of boundedly rational agents to instantiate a causal model of user decision making. In such a model, they articulate how paths in the graph can encourage predictors to exploit non-causal (i.e., spurious) information that doesn't remain stable across distributions. Then, the paper draws upon results from the work on CF invariance to propose regularizers to discourage predictors from using such non-causal information. Since the regularization scheme depends on the causal model, the paper distinguishes two different causal models of users that lead to different regularizers. They empirically study CF invariant perdictors of user decisions with recommendation data and simulated outcomes, finding evidence of robustness to distribution changes and substantiating that indeed, it is important to match regularizers appropriately with causal models.", "questions": "I described the main technical gaps I perceived above. I'll reiterate the points as clear questions for the authors here:\n\n1. Can you clarify whether in definition 1? Does the invariance have to hold for every possible unit (i.e., this is truly counterfactual and would be impossible to verify without assumptions on the full SCM)? If so, why bother since the graph can't identify such a criteria anyway?\n\n2. Can you clarify the motivating figure 2 model, and explain why the $e \\rightarrow y$ link is being considered \"spurious\" or non-causal here? As explained in more detail above, invariant causal learning assumes that the environment has no causal effect on the target $y$ because if it did, environment-invariance isn't intuitively a sensible ask to begin with.\n\n3. Can you clarify that my understanding of S3.3 was right, and clarify why learning a flexible predictor from $\\bar{x}, x$ and $r$ (if $\\bar{x}$ was observed) isn't the optimal thing to do, since we could just learning the (invariant) causal mechanisms directly?\n\n4. Can you better justify how, in the anti-causal model, the user's decision $y$ could causally precede the perception of value? \n\n5. Can you articulate what CI tests you're envisioning to distinguishing between anti-causal and causal user models (presumably, testing to see if $y$ is a collider for $x$ and $r$)? \n\n6. Can you generally clarify proposition 2, perhaps with an example?", "limitations": "As discussed in the weaknesses above, I'd encourage the authors to spend more time discussing the challenges underpinning distinguishing between anti-causal and causal models. Moreover, both in exposition and empirically, it would be good to see more about how to practically deal with the issue of users in the data coming from a mix of these models.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The paper takes a new approach to robust recommendations, making the novel connection that for invariant decision prediction, modeling the user decision making process is what is relevant. In this vein, using ideas from boundedly rational agents, the paper instantiates some causal models of users and articulates what paths in these models incentivize predictors to use spurious information. These connections are non-trivial to develop and can inspire extensions and new veins of work in the recommendation setting.\n2. The empirical studies that involve recommender data with simulated outcomes can be considered another contribution in this paper. Such experimental evaluation could also provide new tasks and baselines for those looking to work on the problem of invariant recommendations.\n3. The writing is generally clear and the authors do a good job or motivating technical ideas with examples and intuitions. The authors seem to have to covered related work well too.\n\nWeaknesses:\n1. In applying and extending ideas about CF invariance, there are some technical gaps and ambiguities throughout the paper that leave me doubting the soundness of some ideas. I'll enumerate:\n+ the notion of CF invariance has always been a bit poorly formalized. Consider definition 1. Does the invariance have to hold for every possible unit (i.e., this is truly counterfactual and would be impossible to verify without assumptions on the full SCM)? Or, does $f_u(\\cdot)$ have to be invariant for all values of x and r, i.e., $P(f_u(x(e), r(e) | X=x, R=r; \\theta) = P(f_u(x(e'), r(e') | X=x, R=r; \\theta) \\forall x, r$? Or, do you want it to hold on average across interventions to $e$, i.e., $P(f_u(x(e), r(e); \\theta) = P(f_u(x(e'), r(e'); \\theta)$? Definition 1 isn't well defined enough to make these distinctions clear. Note that causal graphs alone could only identify versions 2 or 3 of this invariance.\n+ In the same vein, if definition 1 is intended to be truly counterfactual (i.e., must be satisfied for every possible unit specified by the SCM), since the causal graph can never identify it anyway, why bother? Why not just pick a population-level (i.e., interventional as in version 2 or 3 above) version of invariance to satisfy?\n+ The motivating causal model in figure 2 is a bit strange because in this model, the decision truly is causally affected by the environment. In this case, a predictor that satisfies CF invariance in Definition 1 would not capture the causal process of user decision making, which is often the underpinning of invariant causal learning. In fact, much of the work on invariant causal learning begins by assuming that the environment has no causal effect on the target $y$ because if it did, environment-invariance isn't intuitively a sensible ask to begin with. However, in this paper, the $e \\rightarrow y$ motivation persists and is described as the main pathway by which predictors exploit spurious information. It's not non-causally spurious information in this case, though, so the motivation is quite confusing.\n+ The notation in S3.1 and figure 2 tend to obfuscate the fact that what we want is for some of the *information* in $x$ and $r$ to be discarded. That is, we want to partition the information in those variables into a part that a desirable predictor $f_u(\\cdot)$ can use and a part that $f_u(\\cdot)$ should't use. For example, the work by Veitch et al. makes this information decomposition explicit, thereby allowing us to reason about what graphical relationships the \"nice\" part of $x, r$ have to other variables. By being more coarse-grained and only encoding $x$ and $r$, however, it's not easy to reason about what parts of the variables an invariant predictor ought to use, and what relationships the parts have to other variables in the graph.\n+ In S3.2 on \"rational users\", the authors say that a rational user model is akin to assuming a causal graph in which there is no direct arrow from $e$ to $y$, which handicaps the ability to avoid spurious pathways. This statement is confusing. A causal model with no arrow from $e$ to $y$ seems like the best case scenario because any flexible predictor from $x$ and $r$ to $y$ would be able to capture the causal mechanisms by which $y$ is produced. Perhaps a fairer assessment here is that although rational users constitute a best case scenario, this is not a likely model of users.\n+ In S3.3, it seems like if we had the function $v_u(x, \\bar{x}, r)$ (which doesn't depend on the environment), then I could construct an invariant predictor. But, since $\\bar{x}$ is latent, a predictor is incentivized to reconstruct $\\bar{x}$ information using the observed variables $x$ and $r$. However, since $e$ leaves its imprint on $x$ and $r$ and also affects $\\bar{x}$, a predictor might just memorize $e$-relevant information instead. However, the authors say that using $\\bar{x}$ if it *was* observed would be bad because it could lead to overfitting -- this doesn't match my intuition because I'd have thought that observing $\\bar{x}$ is a best-case scenario because then, all paths from $e$ to $y$ are blocked and any flexible predictor could capture the causal mechanisms governing $y$ from $x, \\bar{x}$ and $r$.\n+ In S4.1, the story of the anti-causal user doesn't make sense to me because $v_u(x, \\bar{x}, r)$ sounds like reflects the user's perception of value -- how could the user's decision $y$ possibly causally precede the perception of value? Put differently, the notation may be using $v_u$ to conflate true and perceived value.\n+ In Prop 1, $y$ and $e$ being confounded is not $e \\rightarrow y$. The authors should clarify what they mean here.\n\n2. The paper relegates an important limitation to just 1 sentence. In S4.1, after articulating how the choice of anti-causal versus causal matters for regularization, the authors state that ``this can be done using focused interventions (e.g., A/B testing), or for observational data, using simple conditional independence tests.'' Conducting A/B tests to determine a causal arrow direction for each user is impractical. It's important to spend more time, then, discussing the orientation of edges using CI tests and addressing all the limitations therein. Presumably the idea is to test for v-structures between $x$ and $r$, and $y$? This needs to be explained more directly since in general, causal structure learning won't orient all edges.\n\n3. In a similar vein to above, the empirical studies focus on one or the other causal model (anti-causal vs causal), without addressing the challenge the authors themselves motivated, that typically, a training distribution consists of a mixture of these two causal model modalities. I would love to see this issue tackled more.\n\n3. Section 4.2 could do with a figure that helps to illustrate the proposition and the reasoning about v-structures. This section is hard to follow especially because we don't have the notation to talk about the parts of information that a CF invariant predictor uses. That being said, this proposition seems to provide a novel insight beyond the existing work of CF invariance, so perhaps it would have been more interesting in this paper to dwell on the differences between skeptics and believers than between anti-causal and causal users.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "U3H8haJHnHP", "reviewer": "Reviewer_zV27", "summary": "This paper proposes a learning framework that is robust to environment changes to predict the relevance of items to users. The key idea is to model users as reasoning about decisions through a causal graph and show how minimal information regarding the graph can be used to contend with distributional changes. ", "questions": "It seems the prior knowledge regarding users' causal beliefs (causal or anti-causal) needs to be known ahead for modeling. How can such information be obtained in real-world tasks?", "limitations": "N/A", "rating": 6, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The proposed framework is novel and the paper is fairly well written, and experimental results support the paper's statement that adopting users' causal beliefs results in better prediction performances.\n\nHowever, there are no comparisons with other exciting works. According to section 2 related works, there exist, several models that adopt causal inference techniques for recommendation tasks, the paper would be more sound if the prediction performances are compared against those works.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept