Causal ABM: A Methodology for Learning Plausible Causal Models using Agent-Based Modeling
We present Causal ABM, a methodology to derive causal structures describing complex underlying behavioral phenomena. Agent-based models (ABMs) have powerful advantages for causal modeling that have not been explored sufficiently. Unlike traditional causal estimation approaches which often result in "one best" causal structure that is learned, two properties of ABMs - equifinality (the ability of different sets of conditions or model representations to yield the same outcome) and mutlifinality (the same ABM might yield different outcomes) - can be exploited to learn multiple diverse "plausible causal models" from data. Using an illustrative example of news sharing on social networks we show how this idea can be applied to learn such causal sets. We also show how genetic algorithms can be used as a estimation technique to learn multiple plausible causal models from data due to their parallel search structure. However, significant computational challenges remain before this can be generally applied, and we, therefore, highlight specific key issues that need to be addressed in future work.
Konstantina Valogianni is an Assistant Professor of Information Systems at IE University. She has received her PhD from Rotterdam School of Management, Erasmus University Rotterdam (2016). Her research focuses on using Machine Learning to enable sustainable societies. Her main line of research focuses on designing intelligent algorithms to facilitate a better electric mobility integration in current smart grids. Her work has appeared in journals, such as Information Systems Research, Production and Operations Management, Information & Management, Decision Support Systems, Energy Policy, as well as conferences such as the International Conference on Information Systems (ICIS), AAAI Conference on Artificial Intelligence (AAAI), the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). She is teaching technology and innovation management and machine learning courses at the Masters and Executive levels, whereas she also teaches PhD courses on information systems.