Causal inference for time series analysis: problems, methods and evaluation

Causal Inference in Time Series Analysis

The identification of causal relationships is an important part of scientific research and essential for understanding the consequences when moving from empirical findings to actions. At the same time, the notion of causality has shown to be evasive when trying to formalize it. Among the many properties a general definition of causality should or should not have, there are two important aspects...

a time-series analysis of the demand for life insurance in iran

با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند

A Novel Causal Inference Method for Time Series

Problems of Statistical Estimation and Causal Inference in Time-Series Regression Models

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Fast and Accurate Causal Inference from Time Series Data

Causal inference from time series data is a key problem in many fields, and new massive datasets have made it more critical than ever. Accuracy and speed are primary factors in choosing a causal inference method, as they determine which hypotheses can be tested, how much of the search space can be explored, and what decisions can be made based on the results. In this work we present a new causa...