Causal Inference in Deep Learning Forecasting: A Bayesian Approach to Analyzing the Relationship between Employment Rate and Immigration

Mass migration, caused by events such as climate change or global conflicts significantly impacts every aspect of society including social infrastructures, public service delivery, education, healthcare, and security to list a few which in turn can affect the employment market. This paper presents an innovative methodology to illustrate the causal relationship between migration and employment rates in Canada. We propose a combination of training recurrent neural networks and temporal convolutional networks for employment trend forecasting and populating Bayesian networks to reveal interrelationships across various causal networks. Applying our method shows how changes in demographics such as sex, age, education, and the country of origin of displaced individuals influence the employment rate of the host country.