7 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…
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…
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…
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…
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…