Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
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利用条件生成模型对可识别因果干预分布进行采样
- 研究问题
- 因果图含高维变量和未观测混杂时，如何在难以估计条件似然的情况下，从可识别的条件或无条件干预分布中采样？
- 方法与思路
- 作者沿用基于似然的识别算法递归步骤，训练并连接条件生成模型，通过一系列推前运算生成目标干预分布的样本。
- 材料陈述的贡献
- 摘要称，该算法适用于任意因果图中的可识别干预分布，并展示图像干预、生成模型伪相关分析及文本图像联合应用。
- 阅读关注点
- 应核对可识别性如何判定、递归步骤如何转换为生成操作，以及模型近似误差如何影响最终干预分布的采样质量。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收均未核实；结论限定于可识别分布，摘要未说明误差保证或具体实验数值。
原文摘要
Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods, which is difficult to estimate for high-dimensional (particularly image) data. Researchers have alleviated this issue by simulating causal relations with neural models. However, when we have high-dimensional variables in the causal graph along with some unobserved confounders, no existing work can effectively sample from the un/conditional interventional distributions. In this work, we show how to sample from any identifiable interventional distribution given an arbitrary causal graph through a sequence of push-forward computations of conditional generative models, such as diffusion models. Our proposed algorithm follows the recursive steps of the existing likelihood-based identification algorithms to train a set of feed-forward models, and connect them in a specific way to sample from the desired distribution. We conduct experiments on a Colored MNIST dataset having both the treatment ($X$) and the target variables ($Y$) as images and sample from $P(y|do(x))$. Our algorithm also enables us to conduct a causal analysis to evaluate spurious correlations among input features of generative models pre-trained on the CelebA dataset. Finally, we generate high-dimensional interventional samples from the MIMIC-CXR dataset involving text and image variables.