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 …

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 …

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 …

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 …

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 …

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 …

Instrumental variable methods are often used for parameter estimation in the presence of
confounding. They can also be applied in stochastic processes. Instrumental variable …

Conditional independence is often used as a testable implication of causal models of
random variables. In addition, equality constraints have been proposed to distinguish …

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 …

When dealing with time series data, causal inference methods often employ structural vector
autoregressive (SVAR) processes to model timeevolving random systems. In this work, we …