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Causal Inference and Recommendations

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Machine Learning for Causal Inference

Abstract

In the era of information overload, recommender systems (RSs) have become an indispensable part of online service platforms. Traditional RSs estimate user interests and predict their future behaviors by utilizing correlations in the observational historical activities, their profiles, and the content of interacted items. However, since the inherent causal reasons that lead to the observed users’ behaviors are not considered, multiple types of biases could exist in the generated recommendations. In addition, the causal motives that drive user activities are usually entangled in these RSs, where the explainability and generalization abilities of recommendations cannot be guaranteed. To address these drawbacks, recent years have witnessed an upsurge of interest in enhancing traditional RSs with causal inference techniques. In this chapter, we provide a systematic overview of causal RSs and help readers gain a comprehensive understanding of this promising area. We start with the basic concepts of traditional RSs and their limitations due to the lack of causal reasoning ability. We then discuss how different causal inference techniques can be introduced to address these challenges, with an emphasis on debiasing, explainability promotion, and generalization improvement. Furthermore, we thoroughly analyze various evaluation strategies for causal RSs, focusing especially on how to reliably estimate their performance with biased data if the causal effects of interests are unavailable. Finally, we provide insights into potential directions for future causal RS research.

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Notes

  1. 1.

    We use the term item in a broad sense to refer to anything recommendable to users, such as news [38], jobs [47], articles [68], music [95], movies [20], micro-videos [84], PoIs [93], hashtags [17], etc.

  2. 2.

    We use rating to refer to any user–item interaction that can be represented by a numerical value. This includes both explicit feedback such as likes/dislikes and implicit feedback such as views and clicks. When \(r_{ij}\) represents implicit feedback, the missing elements \(r^{0}_{ik}\) in \(\mathbf {R}\) may be used as weak negative feedback in the training phase [22]. This may complicate the causal problems. Therefore, we assume RSs are trained on observed ratings to simplify the discussion unless specified otherwise.

  3. 3.

    However, we do not distinguish random variables and their specific realizations if there is no risk of confusion. For simplicity, we assume R to be Gaussian unless specified otherwise.

  4. 4.

    For works that do not explicitly treat \(r_{ij}\) as a random variable, we assume it follows a Gaussian distribution with zero variance. The generative process then becomes as \(r_{ij} = {\mathbf {u}}_{i}^{T} \cdot {\mathbf {v}}_{j}\).

  5. 5.

    which can be attributed to multiple reasons such as users’ self-search [75], the recommendations of previous models [37], the position where the items are displayed [76], item popularity [1], etc. Generally, RCM-based causal RSs are agnostic to the specific reason that causes the exposure bias.

  6. 6.

    In the uplift evaluation of RSs that aims to estimate how recommendations change user behaviors [62], \(r_{ij}(a_{ij}=0)\) may be used to represent user i’s rating to item j through self-searching [59].

  7. 7.

    We can gain an intuition of this claim from Fig. 10.2. Suppose covariates C represent the two-dimensional features (user type, movie type). Given \(C=\mathbf {c}\), \( r_{ij}(a_{ij}=1) \perp a_{ij} \mid \mathbf {c}\) described in Eq. (10.3) is satisfied because in each data stratum specified by \(C=\mathbf {c}\) (i.e., the four \(2 \times 2\) blocks in Fig. 10.2b), \(r_{ij}(a_{ij}=1)\) is constant. Fig. 10.2a shows that for the treatment group \(\mathcal {T}\), \(p(\mathbf {c}|a_{ij}=1)=1/2\) for \(\mathbf {c} \in \mathcal {C}_{1} = \{(\text{horror fan, horror movie}), (\text{romance fan, romance movie})\}\) and \(p(\mathbf {c}|a_{ij}=1)=0\) for \(\mathbf {c} \in \mathcal {C}_{2} = \{(\text{horror fan, romance movie}), (\text{romance fan, horror movie})\}\). In contrast, for the population \(\mathcal {P}\mathcal {O}\), \(p(\mathbf {c})=1/4\) for \(\mathbf {c} \in \mathcal {C}_{1} \cup \mathcal {C}_{2}\). Therefore, in the treatment group \(\mathcal {T}\), user-item pairs with covariates in \(\mathcal {C}_{1}\) are over-represented, while those with covariates in \(\mathcal {C}_{2}\) are under-represented. However, we also note that this case is too extreme to be addressed by RCM, as \(p(\mathbf {c}|a_{ij}=1)=0\) for \(C \in \mathcal {C}_{2}\) violates the positivity assumption mentioned in the attention box.

  8. 8.

    In causal graphs, the subscripts i, j for each node are omitted for simplicity.

  9. 9.

    We also omit the mutually independent exogenous variables for each node and summarize their randomness into the structural equations with probability distributions [15]. Subscript G is used to distinguish structural equations from other conditional relationships that can be inferred from G.

  10. 10.

    This corresponds to the case where item exposures are randomized (see the discussions in Sect. 10.3.1.3), as the user–item pair \((U, V)\) is not determined by other factors associated with R [54].

  11. 11.

    The similarity between this section and Sect. 10.3.1.1 shows us the connection between RCM-based and SCM-based causal RSs, where the claim that when item exposure is not randomized, “observing that an item was exposed to the user per se contains extra information about the user-item pair” is mathematically transformed into the abductive inference of \(\mathbf {c}\) from \({\mathbf {v}}_{j}\) by \(p(\mathbf {c}|{\mathbf {v}}_{j})\).

  12. 12.

    Consider again the toy example in Fig. 10.5. If we know exactly the user type and item type \(\mathbf {c}\) for each user–item pair, the predictions can be unbiased even if the item exposures are non-randomized.

  13. 13.

    https://www.cs.cornell.edu/~schnabts/mnar/

  14. 14.

    https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&did=3

  15. 15.

    https://github.com/chongminggao/KuaiRec

  16. 16.

    http://cail.criteo.com/criteo-uplift-prediction-dataset/

  17. 17.

    https://research.zozo.com/data.html

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Acknowledgements

This work is supported by the National Science Foundation under grants IIS-2006844, IIS-2144209, IIS-2223769, CNS-2154962, and BCS-2228534, the JP Morgan Chase Faculty Research Award, and the Cisco Faculty Research Award.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

    Yaochen Zhu

  2. Department of Computer Science, University of Virginia, Charlottesville, VA, USA

    Jing Ma

  3. Department of Electrical and Computer Engineering, Department of Computer Science, and School of Data Science, University of Virginia, Charlottesville, VA, USA

    Jundong Li

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  1. Yaochen Zhu
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  1. University of Virginia, Charlottesville, VA, USA

    Sheng Li

  2. Ant Group, Hangzhou, China

    Zhixuan Chu

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Zhu, Y., Ma, J., Li, J. (2023). Causal Inference and Recommendations. In: Li, S., Chu, Z. (eds) Machine Learning for Causal Inference. Springer, Cham. https://doi.org/10.1007/978-3-031-35051-1_10

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