Structure learning for cyclic linear causal models
C Améndola, P Dettling, M Drton… - … on Uncertainty in …, 2020 - proceedings.mlr.press
We consider the problem of structure learning for linear causal models based on
observational data. We treat models given by possibly cyclic mixed graphs, which allow for …
observational data. We treat models given by possibly cyclic mixed graphs, which allow for …
Efficient identification in linear structural causal models with auxiliary cutsets
We develop a polynomial-time algorithm for identification of structural coefficients in linear
causal models that subsumes previous efficient state-of-the-art methods, unifying several …
causal models that subsumes previous efficient state-of-the-art methods, unifying several …
Causal inference on process graphs, part II: Causal structure and effect identification
ND Reiter, J Wahl, A Gerhardus, J Runge - arXiv preprint arXiv …, 2024 - arxiv.org
A structural vector autoregressive (SVAR) process is a linear causal model for variables that
evolve over a discrete set of time points and between which there may be lagged and …
evolve over a discrete set of time points and between which there may be lagged and …
Efficient identification in linear structural causal models with instrumental cutsets
One of the most common mistakes made when performing data analysis is attributing causal
meaning to regression coefficients. Formally, a causal effect can only be computed if it is …
meaning to regression coefficients. Formally, a causal effect can only be computed if it is …
Nested covariance determinants and restricted trek separation in Gaussian graphical models
Directed graphical models specify noisy functional relationships among a collection of
random variables. In the Gaussian case, each such model corresponds to a semi-algebraic …
random variables. In the Gaussian case, each such model corresponds to a semi-algebraic …
Equality constraints in linear hawkes processes
SW Mogensen - Conference on Causal Learning and …, 2022 - proceedings.mlr.press
Conditional independence is often used as a testable implication of causal models of
random variables. In addition, equality constraints have been proposed to distinguish …
random variables. In addition, equality constraints have been proposed to distinguish …
Formalising causal inference in time and frequency on process graphs with latent components
ND Reiter, A Gerhardus, J Wahl, J Runge - arXiv preprint arXiv …, 2023 - arxiv.org
When dealing with time series data, causal inference methods often employ structural vector
autoregressive (SVAR) processes to model time-evolving random systems. In this work, we …
autoregressive (SVAR) processes to model time-evolving random systems. In this work, we …
Graphical representations for algebraic constraints of linear structural equations models
T van Ommen, M Drton - International Conference on …, 2022 - proceedings.mlr.press
The observational characteristics of a linear structural equation model can be effectively
described by polynomial constraints on the observed covariance matrix. However, these …
described by polynomial constraints on the observed covariance matrix. However, these …
On the Complexity of Identification in Linear Structural Causal Models
J Dörfler, B van der Zander, M Bläser… - arXiv preprint arXiv …, 2024 - arxiv.org
Learning the unknown causal parameters of a linear structural causal model is a
fundamental task in causal analysis. The task, known as the problem of identification, asks to …
fundamental task in causal analysis. The task, known as the problem of identification, asks to …
Time-dependent mediators in survival analysis: Graphical representation of causal assumptions
SW Mogensen, OO Aalen, S Strohmaier - arXiv preprint arXiv:2310.04709, 2023 - arxiv.org
We study time-dependent mediators in survival analysis using a treatment separation
approach due to Didelez [2019] and based on earlier work by Robins and Richardson …
approach due to Didelez [2019] and based on earlier work by Robins and Richardson …