Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is possible to recover the causal effect by using certain observed variables to adjust for the effects of confounders. However, without access to the true causal model, finding this adjustment requires brute-force search. In this work, we present an algorithm that exploits auxiliary variables, similar to instruments, in order to find an appropriate adjustment by a gradient-based optimization method. We demonstrate that it outperforms practical alternatives in estimating the true causal effect, without knowledge of the full causal graph.
Identifying causal relationships for a treatment intervention is a fundamental problem in health sciences. Randomized controlled trials (RCTs) are considered the gold standard for identifying causal relationships. However, recent advancements in the theory of causal inference based on the foundations of structural causal models (SCMs) have allowed the identification of causal relationships from observational data, under certain assumptions. Survival analysis provides standard measures, such as the hazard ratio, to quantify the effects of an intervention. While hazard ratios are widely used in …
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Columbia University（哥伦比亚大学）
The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discrete search procedures for selecting acyclic directed mixed graphs (ADMGs), specifically ancestral ADMGs, that encode ordinary conditional independence constraints among the observed variables of the system. However, confounded systems also exhibit more general equality restrictions that cannot be represented via these graphs, placing a limit on the kinds of structures that can be learned using ancestral ADMGs. In this …
Informatics Institute（信息学研究所）；University of Amsterdam（阿姆斯特丹大学）
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment criteria and formulas from the acyclic setting to the general one (i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal effects from observational data that was (partially) gathered under selection bias and cycles. This generalizes the backdoor criterion, the se…
The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
School of Mathematics, Southeast University, Nanjing 210096, China（数学学院，东南大学，南京210096，中国）；School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China（网络科学与工程学院，东南大学，南京210096，中国）
Mila, Université de Montréal（蒙特利尔大学Mila）；Element AI；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interven…
Differentiable Causal Discovery For Latent Hierarchical Causal Models
University of California San Diego（加利福尼亚大学圣迭戈分校）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, re…
A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of the search space. Perhaps for this reason, most research has so far focused on relatively small causal graphs, with up to hundreds of nodes. However, recent advances in fields like biology enable generating experimental data sets with thousands of interventions followed by rich profiling of thousands of variables, raising the opportunity and urgent need f…
InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling
Mathematical Institute, University of Oxford（牛津大学数学研究所）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；School of Mathematical Sciences, University of Nottingham Ningbo China（宁波诺丁汉大学数学科学学院）
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data biases. In many real-life situations, data is collected from different environments, in which the functional relations remain consistent across environments, while the distribution of additive noises may vary. This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based …
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…
Columbia University（哥伦比亚大学）；Irving Institute for Cancer Dynamics（欧文癌症动力学研究所）；Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Department of Statistics, Columbia University（哥伦比亚大学统计系）
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…
Given a causal graph, the do-calculus can express treatment effects as functionals of the observational joint distribution that can be estimated empirically. Sometimes the do-calculus identifies multiple valid formulae, prompting us to compare the statistical properties of the corresponding estimators. For example, the backdoor formula applies when all confounders are observed and the frontdoor formula applies when an observed mediator transmits the causal effect. In this paper, we investigate the over-identified scenario where both confounders and mediators are observed, rendering both estima…
UFID: A Unified Framework for Input-level Backdoor Detection on Diffusion Models
University of Virginia（弗吉尼亚大学）；Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）
It is emphasised that for epidemiological studies where disease incidence is rare, results from conventional proportional hazards models can often correctly estimate causal associations. The well-known "backdoor criteria" from causal-inference is applied to the common epidemiological study of rare diseases with a proportional hazards model, providing an example of when and how estimates from conventional proportional hazards studies can be used. A similar study with the "frontdoor criteria", that allows studies with unmeasured confounders, finds similar results to conventional mediation analys…
ETH Zurich（苏黎世联邦理工学院）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and t…
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data. CGNNs leverage conditional independencies and distributional asymmetries to discover bivariate and multivariate causal structures. CGNNs make no assumption regarding the lack of confounders, and learn a differentiable generative model of the data by using backpropagation. Extensive experiments show their good performances comparatively to the state of the art in observational causal discovery on both simulated and real data, with respect to cause-effect inference, v-structure identif…
Center for Computing Research, Sandia National Laboratories（桑迪亚国家实验室计算研究中心）；Applied Information Sciences Center, Sandia National Laboratories（桑迪亚国家实验室应用信息科学中心）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）
University of Amsterdam（阿姆斯特丹大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Qualcomm AI Research（高通人工智能研究院）
Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneous causal relations between them. In practical applications, however, our measurement or frame rate might be slower than many of the causal effects. This effectively creates "instantaneous" effects and invalidates previous identifiability results. To address this issue, we propos…
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…
Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences
Goethe University Frankfurt（歌德大学法兰克福分校）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；Max Planck Institute for Empirical Aesthetics（马克斯·普朗克实验美学研究所）；SINTEF（挪威科技工业研究院）；University Medical Center Göttingen（哥廷根大学医学中心）；Institute of Computer Science and Campus Institute Data Science, University Göttingen（哥廷根大学计算机科学与数据科学研究所）；Bridging Research in AI and Neuroscience (brAIN), Computer Vision Center（人工智能与神经科学交叉研究中心（brAIN），计算机视觉中心）；Computer Science Department, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机科学系）；Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University Frankfurt（歌德大学法兰克福分校莱茵-美因癫痫中心，神经学系）；Circulant Labs（循环实验室）
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；ETH Zurich（苏黎世联邦理工学院）；KTH Stockholm（斯德哥尔摩KTH皇家理工学院）；DeepMind（深度思维）
Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-shot adaptation. Recent advances in differentiable causal discovery have proposed to factorize the data generating process into a set of modules, i.e. one module for the conditional distribution of every variable where only causal parents are used as predictors. Such a modular decomposition of knowledge enables adaptation to distributions shifts by only updating…
