We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs. We study this problem without the causal sufficiency assumption, using Maximal Ancestral Graphs (MAG) to model the causal graphs, and assuming that we have the ability to actively perform independent single vertex (or atomic) interventions on the entities. If the $M$ underlying (unknown) causal graphs of the entities satisfy a natural notion of clustering, we give algorithms that leverage this property and recovers all the causal graphs using roughly logarithmic in $M$ number of atomic interventions per entity. These are significantly fewer than $n$ atomic interventions per entity required to learn each causal graph separately, where $n$ is the number of observable nodes in the causal graph. We complement our results with a lower bound and discuss various extensions of our collaborative setting.
Incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interventional constraints, which differs fundamentally from interventional data. While interventional data require direct perturbations of variables, interventional constraints encode high-level causal knowledge in the form of inequality constraints on causal effects. For instance, in the Sachs dataset (Sachs et al.\ 2005), Akt has been shown to be activated by PIP3, meanin…
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causality in nonlinear multivariate systems of time series. We model nonlinear interactions in time series using DeepAR and then expose the model to different environments using Knockoffs-based interventions to test model invariance. Knockoff samples are pairwise exchangeable, in-distribution and statistically null variables generated without knowing the response. We test model invariance where we show that the distribution…
University of Massachusetts（马萨诸塞大学）；Massachusetts Institute of Technology（麻省理工学院）
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions. We show that it is possible to implement this approach using a sufficiently expressive probabilistic programming language. Priors are represented using probabilistic programs that generate source code in a domain specific language. Interventions are represented using probabilistic programs that edit this source code to modify the original generative process. This approach makes it straightforward to incorporate data…
We explore how observational and interventional causal discovery methods can be combined. A state-of-the-art observational causal discovery algorithm for time series capable of handling latent confounders and contemporaneous effects, called LPCMCI, is extended to profit from casual constraints found through randomized control trials. Numerical results show that, given perfect interventional constraints, the reconstructed structural causal models (SCMs) of the extended LPCMCI allow 84.6% of the time for the optimal prediction of the target variable. The implementation of interventional and obse…
University of Oxford（牛津大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. This work incorporates recent advances in Bayesian causal discovery into the Bayesian o…
TU Darmstadt（达姆施塔特工业大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Hessian Center for AI (hessian.AI)（黑森人工智能中心 (hessian.AI)）
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consider the problem of learning interventional distributions using sum-product networks (SPNs) that are over-parameterized by gate functions, e.g., neural networks. Providing an arbitrarily intervened causal graph as input, effectively subsuming Pearl's do-operator, the gate function predicts the parameters of the SPN. The resulting interventional SPNs are motivated and illustrated by a structural causal model themed arou…
Can Large Language Models Help Experimental Design for Causal Discovery?
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Hong Kong Baptist University（香港浸会大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Unobserved confounding is one of the main challenges when estimating causal effects. We propose a causal reduction method that, given a causal model, replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that takes values in the same space as the treatment variable, without changing the observational and interventional distributions the causal model entails. This allows us to estimate the causal effect in a principled way from combined data without relying on the common but often unrealistic assumption that all confounders have been observ…
Relaxing Faithfulness with Intervention-Only Causal Discovery
Thayer School of Engineering Dartmouth College（达特茅斯学院塞耶工程学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）；Massachusetts Institute of Technology（麻省理工学院）
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
Warsaw University（华沙大学）；IDEAS NCBR；Jagiellonian University（雅盖隆大学）；ETH Zurich（苏黎世联邦理工学院）；Helmholtz, TU Munich（慕尼黑工业大学亥姆霍兹中心）；Institute of Mathematics, Polish Academy of Sciences（波兰科学院数学研究所）
LUMLIS (Lisbon ELLIS Unit)（里斯本ELLIS单元LUMLIS）；Instituto de Telecomunicações（电信研究所）；Unbabel（Unbabel公司）
Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples and interventions is known, which is often unrealistic. We envision a scenario with an extensive dataset sampled from multiple intervention distributions and one observation distribution, but where we do not know which distribution originated each sample and how the intervention affected the system, \textit{i.e.}, interventions are entirely latent. We prop…
Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement learning environment and we propose a method to deal with spurious correlations. Specifically, our method designs a reward function that incentivizes an agent to do an intervention to find errors in the causal model. The data obtained from doing the intervention is used to impro…
Arizona State University（亚利桑那州立大学）；Army Research Lab（陆军研究实验室）
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph to be given, our proposed framework learns the causal relations from the data and generates samples accordingly. The proposed CAN comprises a two-fold process namely Label Generation Network (LGN) and Conditional Image Generation Network (CIGN). The LGN is a GAN-based architecture which learns and samples from the causal model over labels. The sampled labels are then fed to CIGN, a conditional…
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；IBM Research Zürich（苏黎世IBM研究院）
Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experi…
The ability to answer causal questions is crucial in many domains, as causal inference allows one to understand the impact of interventions. In many applications, only a single intervention is possible at a given time. However, in some important areas, multiple interventions are concurrently applied. Disentangling the effects of single interventions from jointly applied interventions is a challenging task -- especially as simultaneously applied interventions can interact. This problem is made harder still by unobserved confounders, which influence both treatments and outcome. We address this c…
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models
College of Electronic Engineering, National University of Defense Technology（国防科技大学电子工程学院）；Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation（安徽省网络空间安全态势感知与评估重点实验室）；Institute of Computer Application, China Academy of Engineering Physics（中国工程物理研究院计算机应用研究所）
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on, i.e., the goal is just to minimize the number of interventions. Under the linear cost model, we give an algorithm to identify the ancestral relations of the underlying causal graph, achi…
Transforming a causal system from a given initial state to a desired target state is an important task permeating multiple fields including control theory, biology, and materials science. In causal models, such transformations can be achieved by performing a set of interventions. In this paper, we consider the problem of identifying a shift intervention that matches the desired mean of a system through active learning. We define the Markov equivalence class that is identifiable from shift interventions and propose two active learning strategies that are guaranteed to exactly match a desired me…
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab（多媒体信息处理国家重点实验室，计算机科学学院，PKU-Anker LLM实验室）；Peking University（北京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Paul G. Allen School of Computer Science（保罗·G·艾伦计算机科学学院）；University of Washington（华盛顿大学）；Hong Kong University of Science and Technology（香港科技大学）
ETH Zurich（苏黎世联邦理工学院）；Mila（米拉人工智能研究所）；Universite de Montréal（蒙特利尔大学）；GlaxoSmithKline（葛兰素史克）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Google Research（谷歌研究院）；KTH Stockholm（瑞典皇家理工学院）；DeepMind（深度思维）
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or fixed interventional origin. In this work, we introduce an active intervention targeting (AIT) method which enables a quick identification of the underlying causal structure of the data-generating process. Our…
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Department of Systems Biology, Columbia University Irving Institute for Cancer Dynamics, Columbia University（系统生物学系，哥伦比亚大学伊万尼癌症动力学研究所，哥伦比亚大学）；Irving Institute for Cancer Dynamics, Columbia University（伊万尼癌症动力学研究所，哥伦比亚大学）；Department of Computer Science, Columbia University（计算机科学系，哥伦比亚大学）；Department of Biomedical Engineering, Columbia University（生物医学工程系，哥伦比亚大学）；Data Science Institute, Columbia University（数据科学研究所，哥伦比亚大学）
Latent variables often mask cause-effect relationships in observational data which provokes spurious links that may be misinterpreted as causal. This problem sparks great interest in the fields such as climate science and economics. We propose to estimate confounded causal links of time series using Sequential Causal Effect Variational Autoencoder (SCEVAE) while applying Knockoff interventions. Knockoff variables have the same distribution as the originals and preserve the correlation to other variables. This allows for counterfactuals that are more faithful to the observational distribution. …
McGill University（麦吉尔大学）；ETH Zürich（苏黎世联邦理工学院）；GlaxoSmithKline（葛兰素史克）；TU Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）；Laboratory for Information and Decision Systems, MIT（麻省理工学院信息与决策系统实验室）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research（IBM研究院）；Google Research（谷歌研究院）
