论文标题
反事实估计的扩散因果模型
Diffusion Causal Models for Counterfactual Estimation
论文作者
论文摘要
我们考虑了从观察成像数据中进行反事实估计的任务，给定已知的因果结构。特别是，用神经网络量化干预措施对高维数据的因果效应仍然是一个开放的挑战。本文中，我们提出了DIFF-SCM，这是一个深层的结构性因果模型，它基于最新的基于生成能量的模型的进步。在我们的环境中，通过因果模型需要的边缘和条件分布的迭代采样梯度来进行推断。反事实估计是通过首先通过确定性向前扩散来推断潜在变量来实现的，然后使用抗毒物预测变量w.r.t输入的梯度在反向扩散过程中进行介入。此外，我们提出了一个用于评估产生的反事实的度量。我们发现，与MNIST数据上的基准相比，DIFF-SCM产生更现实和最小的反事实，也可以应用于Imagenet数据。代码可用https://github.com/vios-s/diff-scm。
We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for high-dimensional data with neural networks remains an open challenge. Herein we propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. In our setting, inference is performed by iteratively sampling gradients of the marginal and conditional distributions entailed by the causal model. Counterfactual estimation is achieved by firstly inferring latent variables with deterministic forward diffusion, then intervening on a reverse diffusion process using the gradients of an anti-causal predictor w.r.t the input. Furthermore, we propose a metric for evaluating the generated counterfactuals. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data. Code is available https://github.com/vios-s/Diff-SCM.
