arXiv research

Responds to critiques on tests for causal parameter confidence intervals.

New neural network approach for optimizing latent variable models.

Study shows priors are crucial for accurate causal learning from unlabeled data.

New method identifies causal parameters in tree-shaped linear models using cycles.

The study examines causal razors and their logical relations, highlighting a dilemma in causal discovery.

The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.

New method combines experimental and observational data for causal inference.

New approach uses negative controls to estimate causal parameters without completeness conditions.

Estimates causal effects in Gaussian Linear SCMs with finite data.

Extends robust methods for causal inference, improving estimator performance.

New method uses logical relations to derive bounds and inequality constraints from causal models.

We fully develop the concept of causal symmetry introduced in Class. Quant. Grav. 20 (2003) L139. A causal symmetry is a transformation of a Lorentzian manifold (V,g) which maps every future-directed vector onto a future-directed vector. We prove that the set of all causal symmetries is not a group under the usual comp…

New method identifies latent variables with causal dependencies from observed data.

Study assesses hyperparameter tuning for causal inference with DML.

A method estimates causal parameters using a latent variable recovery.

We define a new type of transformation for Lorentzian manifolds characterized by mapping every causal future-directed vector onto a causal future-directed vector. The set of all such transformations, which we call causal symmetries, has the structure of a submonoid. Some of their properties are investigated and we give…

Consider the case that one observes a single time-series, where at each time t one observes a data record O(t) involving treatment nodes A(t), possible covariates L(t) and an outcome node Y(t). The data record at time t carries information for an (potentially causal) effect of the treatment A(t) on the outcome Y(t), in…

Graph-coupled causal Bayesian optimization transfers information across related interventions.

New methods for estimating complex causal effects in econometrics.

Quantum oracles help identify counterfactuals better than classical ones.

DoubleML is a Python library for causal inference using machine learning.

Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that are seemingly non-causal, such as those with the goal of prediction or prevalence estimation, have causal elements, including differential c…

Hybrid LLM generates synthetic data preserving causal parameters.

Study identifies parameters in causal models with latent confounding.

We introduce causal pieces to improve spiking neural networks.

A model learns causal representations from high-dimensional data.

Proposes a method to assess unobserved confounding effects in causal inference.

LOCAL learns dynamic causal structures from time series data efficiently.

Combines causal learning with dynamical systems for practical model identification.

The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model…

New methods calibrate causal estimates using standard predictive models.

Study causal inference under specific sampling methods with monotonicity assumptions.

Based on the recent work \cite{PII} we put forward a new type of transformation for Lorentzian manifolds characterized by mapping every causal future-directed vector onto a causal future-directed vector. The set of all such transformations, which we call causal symmetries, has the structure of a submonoid which contain…

Granger causality is a widely-used criterion for analyzing interactions in large-scale networks. As most physical interactions are inherently nonlinear, we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measuremen…

We improve robust parameter estimation in causal models from observational data.

Proposes a new estimator for causal mediation with continuous treatments.

New analysis shows surprising results on adaptation speed of causal models.

Novel approach constructs differential causal networks from EEG data.

LLM4Causal democratizes causal reasoning via fine-tuned LLMs.

ISAHP discovers instance-level causal structures in event sequences.

Simplified identification methods for causal inference with arbitrary interventional distributions.

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

New method estimates causal structure from sparse data.

In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study c…

Estimates parameters in max-linear Bayesian networks with noise.

Bayesian method tests Granger causality in functional time series.

A new sorting method using $R^2$ values improves causal discovery from noisy data.

Framework sharpens causal effect estimates without external assumptions.