PhD Dissertation Proposal by Barry Nuqoba | Causal-Driven Collaborative Framework for Identification of Variables and Relationships on Life Panel Data

ABOUT THE TALK

This research aims to design a framework for identifying important variables and causal relationships within life panel data. Initially, we designed a causal-driven collaborative framework that merges computer science algorithms with social science insights to pinpoint key variables and their causal links, utilizing data from the Singapore Life Panel (SLP). While our case study shows that the framework works well, it only examines nine variables from the dataset, encompassing eight types of social activities and life satisfaction, hence unable to demonstrate the true potential of the framework and comprehensive life panel data. Advancing this work, I propose subsequent study broadens the framework’s application to systematically select the most relevant well-being indicators from hundreds across four dimensions, aiming to decipher the causal relationships among these factors. Through this, I propose to consolidate these variables into a single well-being index by leveraging well-established social science methodologies, like factor analysis, for post-processing. This index is intended to facilitate continuous monitoring and evaluation of policies. Ultimately, this endeavor strives to bolster life satisfaction and societal advancement by introducing a robust well-being evaluation and improvement tool.

ABOUT THE SPEAKER

Barry Nuqoba is a Ph.D. Candidate at the School of Computing and Information Systems, Singapore Management University, supervised by Professor NGO Chong Wah (Main Supervisor) and Professor Paulin Tay STRAUGHAN (Co-Supervisor). His research interests focus on investigating the collaboration between causal-related algorithms in computer science and domain expertise in social sciences to broaden the impact of both disciplines in addressing real-world challenges.