提出一种高效估算反事实的新方法，提升估计精度与可扩展性。
Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
- 通过最小化上界将方差优化转化为条件分布学习问题。
- 在多种结构因果模型下优于现有重要性采样方法。
- 适用于有代理变量的实际场景，结果无偏且可解释。
我们提出一种名为外生匹配（Exogenous Matching）的重要性采样方法，用于一般设置下可处理且高效的反事实表达估计。通过最小化反事实估计器的公共上界，我们将方差最小化问题转化为条件分布学习问题，从而可与现有条件分布建模方法结合。我们在多种类型和设定的结构因果模型（SCMs）下验证了理论结果，并证明该方法在反事实估计任务中优于其他重要性采样方法。我们还探讨了注入结构先验知识（反事实马尔可夫边界）对结果的影响。最后，将该方法应用于可识别的代理结构因果模型，实证展示了估计结果的无偏性，说明其在实际场景中的适用性。
原文摘要 · Abstract (English)
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance minimization problem into a conditional distribution learning problem, enabling its integration with existing conditional distribution modeling approaches. We validate the theoretical results through experiments under various types and settings of Structural Causal Models (SCMs) and demonstrate the outperformance on counterfactual estimation tasks compared to other existing importance sampling methods. We also explore the impact of injecting structural prior knowledge (counterfactual Markov boundaries) on the results. Finally, we apply this method to identifiable proxy SCMs and demonstrate the unbiasedness of the estimates, empirically illustrating the applicability of the method to practical scenarios.
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