Team Contribution:
Data Simulation, Model implementation, Model evaluation
Recent shifts toward remote work due to global events such as the COVID-19 pandemic have catalyzed an urgent need to explore its effects on mental health.
This project utilizes a simulated dataset designed to emulate the dynamics between mental health and remote work environments, specifically for the purpose of testing various causal inference algorithms.
The simulated relation includes variables such as social isolation rating, average daily work hours, sleep quality, and company support for mental health.
By employing advanced causal algorithms like PC with kernel conditional independence and Fisher-Z tests, Fast Causal Inference (FCI), and Greedy Equivalence Search (GES) integrated with a custom scoring method,
this study seeks to determine which methods most accurately uncover the complex causal relationships.
This approach aims to provide deeper insights into the structural dependencies and seeks to advance our understanding and application of these analytical tools in complex social science relations.
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