9 citations · 14 across the 12 of their papers we have counts for
12 papers
Semiparametric Inference for Half-Trek Estimators in Linear Structural Equation Models
Leopold Mareis, Nils Sturma, Mathias Drton
Linear structural equation models on directed mixed graphs encode causal relationships among variables subject to latent confounding. The half-trek criterion (HTC) provides a graph…
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
Tom Hochsprung, Nils Sturma, Jakob Runge +2
We consider linear structural equation models with explicitly modelled latent variables. In such models, observed and latent variables solve linear equations including stochastic n…
Efficient Symbolic Computations for Identifying Causal Effects
Benjamin Hollering, Pratik Misra, Nils Sturma
Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, iden…
Mapping the causal structure of price formation in Texas's transitioning electricity market
Shiva Madadkhani, Nils Sturma, Mathias Drton +1
Renewable deployment and rising demand from electrification and large digital loads are transforming electricity markets. However, how these developments reshape electricity price…
Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
Nils Sturma, Mathias Drton
We develop a criterion to certify whether causal effects are identifiable in linear structural equation models with latent variables. Linear structural equation models correspond t…
Matching Criterion for Identifiability in Sparse Factor Analysis
Nils Sturma, Miriam Kranzlmueller, Irem Portakal +1
Factor analysis models explain dependence among observed variables by a smaller number of unobserved factors. A main challenge in confirmatory factor analysis is determining whethe…
Singularity-agnostic incomplete U-statistics for testing polynomial constraints in Gaussian covariance matrices
Dennis Leung, Nils Sturma
Testing the goodness-of-fit of a model with its defining functional constraints in the parameters could date back to Spearman (1927), who analyzed the famous "tetrad" polynomial in…
Mixtures of Discrete Decomposable Graphical Models
Yulia Alexandr, Jane Ivy Coons, Nils Sturma
We study mixtures of decomposable graphical models, focusing on their ideals and dimensions. For mixtures of clique stars, we characterize the ideals in terms of ideals of mixtures…
Algebraic Sparse Factor Analysis
Mathias Drton, Alexandros Grosdos, Irem Portakal +1
Factor analysis is a statistical technique that explains correlations among observed random variables with the help of a smaller number of unobserved factors. In traditional full f…
Unpaired Multi-Domain Causal Representation Learning
Nils Sturma, Chandler Squires, Mathias Drton +1
The goal of causal representation learning is to find a representation of data that consists of causally related latent variables. We consider a setup where one has access to data…
Testing Many Constraints in Possibly Irregular Models Using Incomplete U-Statistics
Nils Sturma, Mathias Drton, Dennis Leung
We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because t…
Half-Trek Criterion for Identifiability of Latent Variable Models
Rina Foygel Barber, Mathias Drton, Nils Sturma +1
We consider linear structural equation models with latent variables and develop a criterion to certify whether the direct causal effects between the observable variables are identi…