Publications by authors named "Atsushi Kuribayashi"
Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios.
IEEE Trans Neural Netw Learn Syst
February 2025
Article Synopsis
- Evaluating interventions in multiagent systems, like autonomous vehicles or sports strategies, is complex and often requires estimating the individual treatment effect (ITE) using long-term counterfactual predictions.
- Traditional frameworks often overlook the dynamic relationships between agents and changing variables, leading to inaccurate ITE assessments.
- The proposed approach uses an interpretable counterfactual recurrent network and graph variational recurrent neural networks (GVRNNs), showing improved accuracy in predicting outcomes and optimal timing for interventions in both simulated contexts (like automated vehicles) and real-world scenarios (like basketball plays).
A new end-off type acyclic ligand with four hydroxyethyl arms, 2,6-bis[bis(2-hydroxyethyl)aminomethyl]-4-methylphenol [H(bhmp)], formed dinuclear cobalt(II) complexes [Co(2)(bhmp)(OAc)(2)]BPh(4) (1) and [Co(2)(bhmp)(OBz)(2)]BPh(4) (2). The complex 1.2.
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