Kernel-Based Causal Balancing for Debiased Collaborative Filtering

Core Concepts

The core message of this paper is to propose a novel kernel-based causal balancing method that can adaptively capture the most important balancing functions to achieve unbiased learning in debiased collaborative filtering.

Abstract

The paper addresses the problem of debiased collaborative filtering, where the collected data may contain different types of biases, posing challenges to effectively learning collaborative filtering models that can well represent the target sample populations.
The key insights are:

The authors theoretically analyze the gaps between the causal balancing requirements and existing propensity score learning methods, such as using cross-entropy loss or manually selecting balancing functions.

To bridge these gaps, the authors propose to approximate the balancing functions in the reproducing kernel Hilbert space (RKHS), where any continuous function can be represented. This allows better satisfying the causal balancing constraints.

The authors design an adaptive kernel balancing algorithm that can adaptively select the most important kernel functions to balance, and provide theoretical analysis on the generalization error bounds.

The proposed kernel balancing method can be applied to both pure propensity-based and doubly robust (DR) debiased collaborative filtering methods.

Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed adaptive kernel balancing approach for both IPS and DR estimators, outperforming various baseline methods.

Stats

The prediction error function eu,i can be well approximated by the selected kernel functions in RKHS.
The balancing weights ˆwu,i are upper bounded by a constant C.

Quotes

"To bridge the above gaps, we propose a debiased CF method that can adaptively capture functions that are more in need of being balanced."
"Inspired by these gaps, we propose to approximate the balancing functions in reproducing kernel Hilbert space and demonstrate that, based on the universal property and representer theorem of kernel functions, the causal balancing constraints can be better satisfied."
"We design a novel kernel balancing method that adaptively find the balancing functions that contribute the most to reducing the estimation bias via convex optimization, named adaptive kernel balancing, and derive the corresponding generalization error bounds."

Key Insights Distilled From

Debiased Collaborative Filtering with Kernel-Based Causal Balancing

by Haoxuan Li,C... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19596.pdf

Deeper Inquiries

How can the proposed kernel balancing method be extended to other causal inference tasks beyond collaborative filtering

The proposed kernel balancing method can be extended to other causal inference tasks beyond collaborative filtering by applying the same principles to different domains. One way to do this is by identifying the key features or covariates in the new dataset that need to be balanced to reduce bias. By representing these features in a reproducing kernel Hilbert space (RKHS) and adapting the kernel balancing algorithm to find the optimal balancing functions, it is possible to achieve unbiased estimations in various causal inference tasks. For example, in healthcare research, the kernel balancing method can be used to balance patient characteristics in observational studies to estimate treatment effects accurately. By incorporating the causal balancing constraints and leveraging the universal property of kernel functions, the method can adapt to different datasets and tasks, providing a robust framework for debiasing in various domains.

What are the potential limitations of the kernel-based approach, and how can they be addressed in future work

While the kernel-based approach offers a powerful and flexible framework for balancing functions in causal inference tasks, there are potential limitations that need to be considered. One limitation is the computational complexity of working in high-dimensional RKHS, especially when dealing with large datasets. This can lead to increased computational costs and scalability issues. To address this limitation, future work could focus on developing more efficient algorithms for kernel balancing that can handle high-dimensional data more effectively. Additionally, the choice of kernel function and hyperparameters can impact the performance of the method. Careful selection and tuning of these parameters are essential to ensure optimal debiasing performance. Furthermore, the interpretability of the results from kernel-based methods may be challenging, as the transformations happening in the RKHS may not directly translate to intuitive insights. Future research could explore ways to enhance the interpretability of the results while maintaining the debiasing capabilities of the approach.

What other types of balancing functions or constraints could be incorporated into the kernel balancing framework to further improve the debiasing performance

Incorporating different types of balancing functions or constraints into the kernel balancing framework can further improve the debiasing performance in causal inference tasks. One approach is to consider higher-order moments of the covariates in the balancing process. By extending the kernel functions to capture higher-order interactions between features, the method can achieve more comprehensive balancing of the data distribution. Additionally, incorporating domain-specific constraints or knowledge into the kernel balancing algorithm can enhance the accuracy of the debiasing process. For example, constraints related to causal relationships or domain-specific rules can be integrated into the optimization framework to guide the selection of balancing functions. By incorporating a diverse set of balancing functions and constraints, the kernel balancing method can adapt to a wide range of datasets and tasks, improving the overall robustness and effectiveness of the debiasing process.