Optimizing Global Mobility policies using Causal Inference

GM newly applied policies can now be evaluated and optimized using cutting edge Causal Inference research methods

By Adv. Tsvi Kan Tor, Prof. Gadi Ravid, PhD candidate in Comp .SC. Yoav Kan Tor, and Research Assistants Eden Cohen and Eric Geringer.


The “Optimizing Global Mobility policies using Causal Inference” paper is an article that demonstrates the benefits of integrating Causal Inference methods when conducting research within the field of Global Mobility. This type of integration was already proven to be a success during the Nobel Prize in Economic Sciences 2021 awards. Causal Inference research methods aim to determine the actual effect of a given variable on the overall result within a larger system such as in the Global Mobility field. Recent developments in the methodology used to assert causality in addition to recent technological innovations involving this type of research has resulted in an increase in the popularity of causality-based research and has enhanced the feasibility of its use.

The writers of the article are professionals in the fields of both Global Mobility and Causal Inference-based research, making them well suited to carry out this type of research. The writers encourage those interested in conducting Global Mobility research to reach out to them. They are confident that this unique integration will not only lead to discoveries that would not have been detected otherwise but can also help solve a number of major problems within the field of Global Mobility. Addressing these issues has become increasingly important as a result of widescale changes to the global employment environment due to shortage of employees in certain sectors in many countries, and an increasing number of climate and war refugees.

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