arXiv research

Proposes a new CBO method without known causal graphs.

SCARY dataset generates complex causal scenarios for causality research.

Bayesian method for causal discovery from unknown general interventions.

New algorithm finds causal parent nodes without graph learning.

New method selects direct causal parents from large sets of variables.

Invariant Causal Set Covering Machines avoid spurious associations.

FLOP algorithm speeds up causal structure learning for linear models.

DAG-FOCI learns causal relationships without parametric assumptions.

New method for causal discovery using peeling algorithms for various data types.

The paper shows how instrumental variables can help identify sparse causal effects in linear models.

Modular neural causal models outperform other models in generalization and adaptation.

LOAD discovers optimal adjustments locally for scalable causal inference.

RAMEN corrects observational data biases for multiple environments.

We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computational…

Local discovery method uncovers direct unfairness in complex systems.

New algorithm identifies best intervention without graph knowledge.

New method improves causal discovery in time series with latent confounders.

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic act…

New algorithm speeds up causal discovery for network data.

Estimating causal models from observational data is a crucial task in data analysis. For continuous-valued data, Shimizu et al. have proposed a linear acyclic non-Gaussian model to understand the data generating process, and have shown that their model is identifiable when the number of data is sufficiently large. Howe…

M-CaStLe discovers causal structures in multivariate space-time data.

Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new probability model for discrete Bayesian networks, which we call an embedded Bayesian network classifier or EBNC. The model for a node $Y$ g…

Game theory approach to predicting and responding to interventions based on causal relationships.

New algorithm learns causal graph to minimize regret in bandits without full structure.

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…

A novel method for learning DAGs from positive-valued data.

Identifies root causes of outliers in unknown cyclic graphs.

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work exploits stability of regression coefficients or invariance properties of models across different experimental conditions for reconstructi…

This work sets a universal lower bound for learning causal DAGs with atomic interventions.

The paper proves ML estimators are strongly consistent for identifying edge weights in BAR models.

KaCGM models provide transparent causal inference from tabular data.

Bayesian causal inference method improves accuracy over traditional approaches.

New method identifies extreme risk propagation in financial networks.

DAG-FM discovers causal relationships from heterogeneous data.

Three RFF-based methods for nonlinear causal discovery in mixed data.

We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots,X_n$ 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…

Efficiently learns linear non-Gaussian DAGs with noisy nodes.

Paper introduces VBG for Bayesian causal structure and mechanism learning.

A new measure of causal influence quantifies intrinsic contributions in DAGs.

New algorithm identifies causal effects in latent confounding models.

Bayesian method recovers causal structure in SEMs with equal error variances.

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…

Modeling complex systems with multi-resolution data and causal dependencies.

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

New method uses information theory to uncover causal relationships in complex systems.

This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-co…

Method bounds continuous-valued treatment effects when confounding variables are hidden.

New algorithms for efficient causal interventions with budget constraints and without constraints.