arXiv research

Paper constructs unfaithful probability distributions in binary causal graphs.

AnomalyCD discovers anomaly causes in large systems with binary flags, reducing computational burden.

Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to discover a unique causal model have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limit…

New algorithm reduces regret in combinatorial causal bandits without graph structure.

Improves causal graph learning on dependent binary data.

Bayesian VAR model discovers Granger causality with uncertainty-aware binary graphs.

Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.

We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal grap…

Study identifies conditions for proxy adjustment in confounded binary treatment outcomes.

CausalEGM estimates causal effects by encoding confounders, improving performance in high-dimensional settings.

Study shows how adjusting for a binary proxy can bound causal effects.

Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are…

Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.

This tutorial introduces causal modeling methods for researchers.

StrNN uses neural network structures to learn conditional independencies.

Paper proposes Bayesian TMLE methods for causal effect uncertainty quantification.

This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterized by binary graph adjacency matrix and show that, if the original SEM is identifiable, then the binary adjacency matrix can be identified up…

New model tackles causal bandits with dependent variables.

Bayesian method models binary response and covariates for two groups, estimating causal relationships.

Study develops method for estimating causal effects in continuous variables.

Paper extends FOFC algorithm to work with mixed data types.

NeuralCSA uses neural networks to analyze causal effects under unobserved confounding.

The paper investigates causal relationships in heart failure prediction using machine learning.

We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools from computational algebraic geometry, in particular, the computation of Groebner bases. We focus on causal structures containing just two observed variables, each of which is binary. We consider the consequences of imp…

Quantum oracles help identify counterfactuals better than classical ones.

Invariant Causal Set Covering Machines avoid spurious associations.

We pose causal inference as the problem of learning to classify probability distributions. In particular, we assume access to a collection $\{(S_i,l_i)\}_{i=1}^n$, where each $S_i$ is a sample drawn from the probability distribution of $X_i \times Y_i$, and $l_i$ is a binary label indicating whether "$X_i \to Y_i$" or …

DCRL learns causal relationships from mixed-type discrete data.

The notion of causality is used in many situations dealing with uncertainty. We consider the problem whether causality can be identified given data set generated by discrete random variables rather than continuous ones. In particular, for non-binary data, thus far it was only known that causality can be identified exce…

New model analyzes customer churn with tensor completion and binary data.

Paper relaxes identifiability conditions for causal models with latent variables.

Symmetric observations don't necessarily imply symmetric causal explanations.

New method identifies common cause in causal insufficiency, revealing complex phase transitions.

ML4C uses binary classification to infer causal structures from latent vicinity.

CausalMix generates synthetic data with causal controls for mixed-type tables.

LxCIM metric improves binary classification performance evaluation.

Study causal inference under specific sampling methods with monotonicity assumptions.

TRAM-DAG models bridge interpretability and flexibility in causal modeling.

New analysis identifies key factors in wildfire-generated thunderstorms.

New principle for disentangling latent factors using sparse regularization.

EML-CD discovers causal mechanisms from neural networks in a structured way.

While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence. Designing an efficient causality test, that may be carried out in the absence of restricti…

Sensitivity analysis for individualized effects in OTRs with binary risk factors.

New method learns unbiased treatment representations from structured high-dimensional data.

Proposes a differentiable structure learning framework for general binary data.

Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.

We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order exponential mo…

Study on merging predictors in causal and anticausal directions using CMAXENT.