Survivor bias in observational data leads the optimization of recommender
systems towards local optima. Currently most solutions re-mines existing
human-system collaboration patterns to maximize longer-term satisfaction by
reinforcement learning. However, from the causal perspective, mitigating
survivor effects requires answering a counterfactual problem, which is
generally unidentifiable and inestimable. In this work, we propose a neural
causal model to achieve counterfactual inference. Specifically, we first build
a learnable structural causal model based on its available graphical
representations which qualitatively characterizes the preference transitions.
Mitigation of the survivor bias is achieved though counterfactual consistency.
To identify the consistency, we use the Gumbel-max function as structural
constrains. To estimate the consistency, we apply reinforcement optimizations,
and use Gumbel-Softmax as a trade-off to get a differentiable function. Both
theoretical and empirical studies demonstrate the effectiveness of our
solution.

通过建立可学习的结构因果模型，并使用古贝尔最大函数作为结构约束，应用增强优化和古贝尔 - Softmax 作为权衡手段来实现反事实一致性，从而减轻观察数据的幸存者偏差问题。