We introduce CGNN, a framework to learn functional causal models as generative neural networks. These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data. Unlike previous approaches, CGNN leverages both conditional independences and distributional asymme...
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, David Lopez-Paz, Isabelle Guyon, Mich`ele Sebag, Aris Tritas, and Paola Tubaro. Learning functional causal models with generative neural networks, 2017.Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, David Lopez-Paz, Isabelle Guyon, ...
利用生成对抗网络 (Generative Adversarial Networks, GAN) 框架,生成反事实结果(counterfactual outcome) 并判断真假,基于此再次生成个体的全部潜在结果 (potential outcome) 并判断真假,进而得到处理对结果的因果效应预估值。网络架构由两个部分组成,反事实生成模块 (Counterfactual imputation block) 和 ITE模块 (ITE block...
(2021 ICLR) Counterfactual Generative Networks. Axel Sauer, Andreas Geiger. [pdf] 2.6 Causal Relation ExtractionSurveys and ReviewsCREST: A Causal Relation Schema for Text (A repo containing datasets for causal/counterfactual relation extraction) Pedram Hosseini. [GitHub] [Summary] CausalRE datasets: ...
Code provided to reproduce the results from the article "Learning Functional Causal Models with Generative Neural Networks" Requirements: numpy scipy scikit-learn tensorflow joblib pandas In order to run the CGNN and launch the experiments:
3)deep generative modeling(深度生成式建模) SCM 描述了源于因果关系的数据的生成过程,并且在深度生成模型方面也有优势,GEAR 采用了这一思路 GEAR假设节点的敏感属性对自身和相邻节点都有因果关系。因为没有考虑节点间的因果关系,节点的敏感属性干预图也不是反事实图,需要训练一个公平约束的变分图自动编码器(VGAE)来...
First we train a causal implicit generative model over binary labels using a neural network consistent with a causal graph as the generator. We empirically show that WassersteinGAN can be used to output discrete labels. Later, we propose two new conditional GAN architectures, which we call Causal...
neural network (NN), whose outputs are estimates of the causal indicators. D2CL differs from classical causal structure learning approaches both in terms of the underlying framework (based on causal risk rather than generative causal models) and in leveraging NNs. The assumptions underlying the ...
Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21) machine-learningpytorchgenerative-modelcausal-models UpdatedApr 18, 2022 Python Streamline a data analysis process ...
Model）GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets[10]...