Recommender models aim to capture user preferences from historical feedback
and then predict user-specific feedback on candidate items. However, the
presence of various unmeasured confounders causes deviations between the user
preferences in the historical feedback and the true preferences, resulting in
models not meeting their expected performance. Existing debias models either
(1) specific to solving one particular bias or (2) directly obtain auxiliary
information from user historical feedback, which cannot identify whether the
learned preferences are true user preferences or mixed with unmeasured
confounders. Moreover, we find that the former recommender system is not only a
successor to unmeasured confounders but also acts as an unmeasured confounder
affecting user preference modeling, which has always been neglected in previous
studies. To this end, we incorporate the effect of the former recommender
system and treat it as a proxy for all unmeasured confounders. We propose a
novel framework, \textbf{S}eparating and \textbf{L}earning Latent Confounders
\textbf{F}or \textbf{R}ecommendation (\textbf{SLFR}), which obtains the
representation of unmeasured confounders to identify the counterfactual
feedback by disentangling user preferences and unmeasured confounders, then
guides the target model to capture the true preferences of users. Extensive
experiments in five real-world datasets validate the advantages of our method.

提出了一种名为 SLFR 的新框架，通过解开用户偏好和未测量混淆因素的联系，识别反事实反馈，以捕捉用户的真实偏好，并在五个真实世界数据集上进行了广泛的实验证明了该方法的优势。

分离和学习潜在混淆因素以增强用户偏好建模

Separating and Learning Latent Confounders to Enhancing User Preferences  Modeling

We study how to extend the use of the diffusion model to answer the causal
question from the observational data under the existence of unmeasured
confounders. In Pearl's framework of using a Directed Acyclic Graph (DAG) to
capture the causal intervention, a Diffusion-based Causal Model (DCM) was
proposed incorporating the diffusion model to answer the causal questions more
accurately, assuming that all of the confounders are observed. However,
unmeasured confounders in practice exist, which hinders DCM from being
applicable. To alleviate this limitation of DCM, we propose an extended model
called Backdoor Criterion based DCM (BDCM), whose idea is rooted in the
Backdoor criterion to find the variables in DAG to be included in the decoding
process of the diffusion model so that we can extend DCM to the case with
unmeasured confounders. Synthetic data experiment demonstrates that our
proposed model captures the counterfactual distribution more precisely than DCM
under the unmeasured confounders.

我们研究如何扩展扩散模型的使用，以回答存在未测混淆因素的观测数据下的因果问题。在 Pearl 的使用有向无环图 (DAG) 捕捉因果干预的框架中，提出了一种基于扩散模型的因果模型 (DCM)，以更准确地回答因果问题，前提是所有混淆因素都可以被观察到。然而，在实践中存在未测混淆因素，这限制了 DCM 的适用性。为了减轻 DCM 的这一局限性，我们提出了一种称为基于后门准则的 DCM (BDCM) 的扩展模型，其思想根植于在 DAG 中找到要包括在扩散模型的解码过程中的变量的后门准则，以便我们可以将 DCM 扩展到存在未测混淆因素的情况。合成数据实验证明了我们提出的模型在未测混淆因素下更精确地捕捉了反事实分布。

因果推断中的未观测混淆因素扩散模型

Diffusion Model in Causal Inference with Unmeasured Confounders

This paper studies the confounding effects from the unmeasured confounders
and the imbalance of observed confounders in IV regression and aims at unbiased
causal effect estimation. Recently, nonlinear IV estimators were proposed to
allow for nonlinear model in both stages. However, the observed confounders may
be imbalanced in stage 2, which could still lead to biased treatment effect
estimation in certain cases. To this end, we propose a Confounder Balanced IV
Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured
confounders and the imbalance of observed confounders. Theoretically, by
redefining and solving an inverse problem for potential outcome function, we
show that our CB-IV algorithm can unbiasedly estimate treatment effects and
achieve lower variance. The IV methods have a major disadvantage in that little
prior or theory is currently available to pre-define a valid IV in real-world
scenarios. Thus, we study two more challenging settings without pre-defined
valid IVs: (1) indistinguishable IVs implicitly present in observations, i.e.,
mixed-variable challenge, and (2) latent IVs don't appear in observations,
i.e., latent-variable challenge. To address these two challenges, we extend our
CB-IV by a latent-variable module, namely CB-IV-L algorithm. Extensive
experiments demonstrate that our CB-IV(-L) outperforms the existing approaches.

本论文研究了工具变量回归中未测量混淆因素和观测混淆因素不平衡引起的混淆效应，提出一种混淆平衡的工具变量回归算法 (CB-IV) 来无偏估计治疗效应，进一步提高 CB-IV 的性能，扩展引入了潜在变量模块 (CB-IV-L)，实验证明了 CB-IV (-L) 算法优于现有的方法。