A time series causal model

Cause-effect relations are central in economic analysis. Uncovering empirical cause-effect relations is one of the main research activities of empirical economics. In this paper we develop a time series casual model to explore casual relations among economic time series. The time series causal model is grounded on the theory of inferred causation that is a probabilistic and graph-theoretic approach to causality featured with automated learning algorithms. Applying our model we are able to infer cause-effect relations that are implied by the observed time series data. The empirically inferred causal relations can then be used to test economic theoretical hypotheses, to provide evidence for formulation of theoretical hypotheses, and to carry out policy analysis. Time series causal models are closely related to the popular vector autoregressive (VAR) models in time series analysis. They can be viewed as restricted structural VAR models identified by the inferred causal relations.Inferred Causation, Automated Learning, VAR, Granger Causality, Wage-Price Spiral

Learning from expository text in L2 reading: Memory for casual relations and L2 reading proficiency

This study explored the relation between second-language (L2) readers’ memory for causal relations and their learning outcomes from expository text. Japanese students of English as a foreign language (EFL) with high and low L2 reading proficiency read an expository text. They completed a causal question and a problem-solving test as measures of memory for causal relations and learning from the text, respectively. It was found that memory for causal relations contributed to text learning in high-proficiency readers, but not in low-proficiency readers. The quantitative and qualitative analysis of causal question answers revealed that low-proficiency readers recalled fewer causal relations and made more incorrect inferences than high-proficiency ones. Additionally, low-proficiency readers tended to perform the problem solving using inappropriate causal sequences and irrelevant information. These findings suggest that low-proficiency readers struggled with processes at both textbase and situation-model levels; consequently, they failed to learn causal relations in the text as knowledge

Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables

We propose an approach for learning the causal structure in stochastic dynamical systems with a $1$-step functional dependency in the presence of latent variables. We propose an information-theoretic approach that allows us to recover the causal relations among the observed variables as long as the latent variables evolve without exogenous noise. We further propose an efficient learning method based on linear regression for the special sub-case when the dynamics are restricted to be linear. We validate the performance of our approach via numerical simulations

From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders

We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders. We assume a discrete-time, stationary structural vector autoregressive process, with both temporal and contemporaneous causal relations. One may ask if temporal and contemporaneous relations should be treated differently. The presented algorithm gradually refines a causal graph by learning long-term temporal relations before short-term ones, where contemporaneous relations are learned last. This ordering of causal relations to be learnt leads to a reduction in the required number of statistical tests. We validate this reduction empirically and demonstrate that it leads to higher accuracy for synthetic data and more plausible causal graphs for real-world data compared to state-of-the-art algorithms.Comment: Proceedings of the 40-th International Conference on Machine Learning (ICML), 202