Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, and robustness. To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction. Specifically, we introduce latent variables that are separated into (a) output-causative factors and (b) others that are spuriously correlated to the output via confounders, to model the underlying causal factors. We further assume the generating mechanisms from latent space to observed data to be causally invariant. We give the identifiable claim of such invariance, particularly the disentanglement of output-causative factors from others, as a theoretical guarantee for precise inference and avoiding spurious correlation. We propose a Variational-Bayesian-based method for estimation and to optimize over the latent space for prediction. The utility of our approach is verified by improved interpretability, prediction power on various OOD scenarios (including healthcare) and robustness on security.
翻译:目前受监督的学习在数据配置过程中可以发现虚假的关联,在解释性、分配外(OOD)一般化和稳健性方面造成问题。为了避免虚假的关联,我们提议采用一个追求因果预测的隐性可变因素(LaCIM)模型(LaCIM),具体地说,我们引入了以下潜在变量:(a) 产出因果因素,和(b) 与通过混凝土生成结果有虚假关联的其他变量,以模拟根本的因果因素。我们进一步假定从潜在空间产生机制,到观察到的数据都是因果性的。我们提出这种差异的可辨别说法,特别是将产出因果因素与他人分离作为精确推断和避免虚假关联的理论保证。我们提出了一种基于Varical-Bayesian法的估算和优化潜在预测空间的方法。我们的方法的效用通过改进可解释性、对各种OOD情景(包括保健)的预测力和安全的稳健性得到验证。