In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated.
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.
Learning causal relations from observational time series with nonlinear interactions and complex causal structures is a key component of human intelligence, and has a wide range of applications.
This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually.
We consider the problem of inferring causal relationships between two or more passively observed variables.
Testing for conditional independence is a core aspect of constraint-based causal discovery.
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.
In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.
In this paper, I relax the single DAG assumption by modeling causal processes using a mixture of DAGs so that the graph can change over time.