Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Siyuan Guo*, Viktor Tóth*, Bernhard Schölkopf, Ferenc Huszár (2022)
Abstract
Learning invariant causal structure often relies on conditional independence testing and assumption of independent and identically distributed data. Recent work has explored inferring invariant causal structure using data coming from different environments. These approaches are based on independent causal mechanism (ICM) principle which postulates that the cause mechanism is independent of the effect given cause mechanism. Despite its wide application in machine learning and causal inference, there lacks a statistical formalization of what independent mechanism means. Here we present Causal de Finetti which offers a first statistical formalization of ICM principle.
Teams Frightened of Failure Fail More: Modelling Reward Sensitivity in Teamwork
Siyuan Guo, Soo Ling Lim, Peter J. Bentley
Accepted at IEEE Symposium Series on Computational Intelligence (SSCI 2020)
Abstract
According to Gray’s Reinforcement Sensitivity Theory (RST), individuals have differing sensitivities to rewards and punishments, which in turn affect their behaviours. The behavioural inhibition system (BIS) is associated with sensitivity to punishment while the behavioural activation system (BAS) is associated with sensitivity to reward. In this work, we model BIS/BAS by supplementing an existing agent-based model of team collaboration in order to explore the combined effect on team performance for a more complex and realistic personality structure. We investigate the significance of BIS/BAS on team behaviour for tasks with differing levels of uncertainty. Findings include a prediction that for tasks with uncertainty, a majority of personality types are significantly influenced by behavioural activation system, and that all personality types are significantly negatively influenced by behavioural inhibition system. The more sensitive to punishments, the worse teams perform.