Causal learning has long concerned itself with the accurate recovery of
underlying causal mechanisms. Such causal modelling enables better explanations
of out-of-distribution data. Prior works on causal learning assume that the
high-level causal variables are given. However, in machine learning tasks, one
often operates on low-level data like image pixels or high-dimensional vectors.
In such settings, the entire Structural Causal Model (SCM) -- structure,
parameters, \textit{and} high-level causal variables -- is unobserved and needs
to be learnt from low-level data. We treat this problem as Bayesian inference
of the latent SCM, given low-level data. For linear Gaussian additive noise
SCMs, we present a tractable approximate inference method which performs joint
inference over the causal variables, structure and parameters of the latent SCM
from random, known interventions. Experiments are performed on synthetic
datasets and a causally generated image dataset to demonstrate the efficacy of
our approach. We also perform image generation from unseen interventions,
thereby verifying out of distribution generalization for the proposed causal
model.

本文提出了一种基于贝叶斯推理的方法，在低级别数据的情况下学习维度高、SCM 结构和参数未知的高级因果变量，从而实现对潜在 SCM 的推断和图像生成。

学习潜在结构因果模型

Learning Latent Structural Causal Models

Learning predictors that do not rely on spurious correlations involves
building causal representations. However, learning such a representation is
very challenging. We, therefore, formulate the problem of learning a causal
representation from high dimensional data and study causal recovery with
synthetic data. This work introduces a latent variable decoder model, Decoder
BCD, for Bayesian causal discovery and performs experiments in mildly
supervised and unsupervised settings. We present a series of synthetic
experiments to characterize important factors for causal discovery and show
that using known intervention targets as labels helps in unsupervised Bayesian
inference over structure and parameters of linear Gaussian additive noise
latent structural causal models.

该研究探讨了从高维数据中学习因果表示的挑战性问题，并引入了一种基于潜变量解码器模型的贝叶斯因果发现方法，在半监督和无监督的情况下进行了实验，表明使用已知的干预目标能够帮助结构和参数的无监督贝叶斯推断。