A step towards the applicability of algorithms based on invariant causal learning on observational data
Machine learning can benefit from causal discovery for interpretation and from causal inference for generalization. In this line of research, a few invariant learning algorithms for out-of-distribution (OOD) generalization have been proposed by using multiple training environments to find invariant relationships. Some of them are focused on causal discovery as Invariant Causal Prediction (ICP), which finds causal parents of a variable of interest, and some directly provide a causal optimal predictor that generalizes well in OOD environments as Invariant Risk Minimization (IRM). This group of algorithms works under the assumption of multiple environments that represent different interventions in the causal inference context. Those environments are not normally available when working with observational data and real-world applications. Here we propose a method to generate them in an efficient way. We assess the performance of this unsupervised learning problem by implementing ICP on simulated data. We also show how to apply ICP efficiently integrated with our method for causal discovery. Finally, we proposed an improved version of our method in combination with ICP for datasets with multiple covariates where ICP and other causal discovery methods normally degrade in performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryCausal InferenceSimilar Papers 제목 키워드 기반
Coarsening Linear Non-Gaussian Causal Models with Cycles
Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal structure in terms of a low-dimensional one. E…
Deep Convolutional Neural Networks for Pairwise Causality
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning. As has been shown before, the direction of pairwise causal relations…
AttributeCausal DiscoverycounterfactualCounterfactual ReasoningComplete Causal Identification from Ancestral Graphs under Selection Bias
Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, modeled by directed…
CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Improving generalization and achieving highly predictive, robust machine learning models necessitates learning the underlying causal structure of the variables of interest. A prominent and effective method for this is le…
Out-of-Distribution GeneralizationA Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference
Identifying cause-effect relations among variables is a key step in the decision-making process. While causal inference requires randomized experiments, researchers and policymakers are increasingly using observational s…
Causal InferenceDecision Making