This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two well-known algorithms, namely PC and FCI. The behavior of our methods is illustrated through several experiments run on simulated and real datasets
We propose different approaches to infer causal influences between agents in a network using only ob...
This paper is concerned with the problem of making causal inferences from observational data, when t...
We investigate how efficiently a known underlying sparse causality structure of a simulated multivar...
International audienceThis study addresses the problem of learning an extended summary causal graph ...
International audienceThis study addresses the problem of learning a summary causal graph on time se...
This study addresses the problem of learning a summary causal graph on time series with potentially ...
International audienceWe address, in the context of time series, the problem of learning a summary c...
Standard causal discovery methods must fit a new model whenever they encounter samples from a new un...
International audienceCausality defines the relationship between cause and effect. In multivariate t...
Identifying causality in multivariate time-series data is a topic or significant interest due to its...
We describe an approach to learning causal models that leverages temporal information. We posit the ...
International audienceWe introduce in this survey the major concepts, models, and algorithms propose...
Abstract. In time series analysis, inference about cause-effect relation-ships among multiple time s...
International audienceCausality defines the relationship between cause and effect. In multivariate t...
Applying a probabilistic causal approach, we define a class of time series causal models (TSCM) base...
We propose different approaches to infer causal influences between agents in a network using only ob...
This paper is concerned with the problem of making causal inferences from observational data, when t...
We investigate how efficiently a known underlying sparse causality structure of a simulated multivar...
International audienceThis study addresses the problem of learning an extended summary causal graph ...
International audienceThis study addresses the problem of learning a summary causal graph on time se...
This study addresses the problem of learning a summary causal graph on time series with potentially ...
International audienceWe address, in the context of time series, the problem of learning a summary c...
Standard causal discovery methods must fit a new model whenever they encounter samples from a new un...
International audienceCausality defines the relationship between cause and effect. In multivariate t...
Identifying causality in multivariate time-series data is a topic or significant interest due to its...
We describe an approach to learning causal models that leverages temporal information. We posit the ...
International audienceWe introduce in this survey the major concepts, models, and algorithms propose...
Abstract. In time series analysis, inference about cause-effect relation-ships among multiple time s...
International audienceCausality defines the relationship between cause and effect. In multivariate t...
Applying a probabilistic causal approach, we define a class of time series causal models (TSCM) base...
We propose different approaches to infer causal influences between agents in a network using only ob...
This paper is concerned with the problem of making causal inferences from observational data, when t...
We investigate how efficiently a known underlying sparse causality structure of a simulated multivar...