A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
We identify direct causes of a target variable from observational data without full DAG identifiability.
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been proposed. However, most of these deal only with purely directed causal relationships and cannot det…
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable \emph{T…
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause and the conditional distribution mapping cause to effect as independent random variables, we propose to infer the causal direction via comparing the di…
Framework identifies causal direction from single data setting.
New method improves cause-effect identification using neural networks.
New method learns graph structure with hidden causes from observational data.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
Given data over the joint distribution of two random variables and , we consider the problem of inferring the most likely causal direction between and . In particular, we consider the general case where both and may be univariate or multivariate, and of the same or mixed data types. We take an inf…
AB-SAGA optimizes distributed optimization over directed graphs using variance reduction and stochastic weights.
We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of …
Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.
New method identifies common cause in causal insufficiency, revealing complex phase transitions.
A novel circuit motif uses sister cells for inference with correlated priors.
Paper introduces a method to infer causal direction from limited data.
New method identifies causes in time series with latent variables.
New method uses kernel deviance measures to discover causal relationships in heterogeneous data.
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal relationship/structure among variables. In recent years, an Independent Mechanism (IM) principle was proposed, stating that the mechanism gene…
Develops SCMs for latent selection to simplify causal analysis.
This study examines how noise levels affect causal discovery methods.
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables and from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this …
New method improves bivariate causal discovery by accurately estimating cause variable complexity.
Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods discover causal variables by measuring each variable's degree of statistical dependence with the target using dependence measures (DMs). Howev…
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presu…
We propose a robust method to estimate heteroscedastic noise models using Student's t-distribution.
New criteria distinguish cause from effect in data, overcoming statistical limitations.
The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize the…
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation …
Study evaluates how noise affects ANMs' ability to identify causal directions.
NetRCA algorithm locates network faults by analyzing derived features and leveraging unlabeled data.
New method identifies cause and effect using complexity of autoencoders.
In optimization, the negative gradient of a function denotes the direction of steepest descent. Furthermore, traveling in any direction orthogonal to the gradient maintains the value of the function. In this work, we show that these orthogonal directions that are ignored by gradient descent can be critical in equilibri…
Study on merging predictors in causal and anticausal directions using CMAXENT.
We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the vari…
Identifying causal direction in location-scale noise models with hidden variables
Push-SAGA is a decentralized algorithm for directed graphs that converges linearly.
A new method identifies causal direction using dense functional classes.
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude ca…
Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transit…
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying d…
In surface mount technology (SMT), mounted components on soldered pads are subject to move during reflow process. This capability is known as self-alignment and is the result of fluid dynamic behaviour of molten solder paste. This capability is critical in SMT because inaccurate self-alignment causes defects such as ov…
By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal model has demonstrated its excellent performance in distinguishing the cause from effect. However, its identifiability has not been properly …
We describe a formal approach to identify 'root causes' of outliers observed in variables in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG). To this end, we first introduce a systematic way to define outlier scores. Further, we introduce the concep…
We propose a method to learn causal response representations through direct effect analysis.
Study identifies and estimates causal LSNM models, proving feature maps are consistent.
New analysis shows surprising results on adaptation speed of causal models.
Distributions over permutations arise in applications ranging from multi-object tracking to ranking of instances. The difficulty of dealing with these distributions is caused by the size of their domain, which is factorial in the number of considered entities (). It makes the direct definition of a multinomial dist…
We propose a new Directed Continuous-Time Random Walk (CTRW) model with memory. As CTRW trajectory consists of spatial jumps preceded by waiting times, in Directed CTRW, we consider the case with only positive spatial jumps. Moreover, we consider the memory in the model as each spatial jump depends on the previous one.…