We propose a Bayesian nonparametric (BNP) approach to causal inference using observational data consisting of outcome, treatment, and a set of confounders. The conditional distribution of the outcome given treatment and confounders is modeled flexibly using a dependent nonparametric mixture model, in which both the atoms and the weights vary with the confounders. The proposed BNP model is well suited for causal inference problems, as it does not rely on parametric assumptions about how the conditional distribution depends on the confounders. In particular, the model effectively adjusts for confounding and improves the modeling of treatment effect heterogeneity, leading to more accurate estimation of both the average treatment effect (ATE) and heterogeneous treatment effects (HTE). Posterior inference under the proposed model is computationally efficient due to the use of data augmentation. Extensive evaluations demonstrate that the proposed model offers competitive or superior performance compared to a wide range of recent methods spanning various statistical approaches, including Bayesian additive regression tree (BART) models, which are well known for their strong empirical performance. More importantly, the model provides fully probabilistic inference on quantities of interest that other methods cannot easily provide, using their posterior distributions.

翻译：我们提出一种基于观测数据（包含结果变量、处理变量及一组混杂变量）的贝叶斯非参数因果推断方法。通过依赖非参数混合模型，我们灵活建模了给定处理变量与混杂变量时结果变量的条件分布，其中原子成分与权重均随混杂变量变化。所提出的贝叶斯非参数模型特别适用于因果推断问题，因其不依赖于条件分布与混杂变量关系的参数化假设。该模型能有效校正混杂偏倚，并改进处理效应异质性的建模，从而更精确地估计平均处理效应与异质性处理效应。基于数据增广技术，该模型的后验推断计算效率显著。大量评估表明，相较于包括贝叶斯加性回归树模型在内的多种近期统计方法（该模型以优异的实证性能著称），所提模型展现出具有竞争力或更优越的性能。更重要的是，该模型能通过后验分布对目标量进行完全概率推断，这是其他方法难以实现的。