Optimal Treatment Regimes for Proximal Causal Learning

A common concern when a policymaker draws causal inferences from and makes decisions based on observational data is that the measured covariates are insufficiently rich to account for all sources of confounding, i.e., the standard no confoundedness assumption fails to hold. The recently proposed proximal causal inference framework shows that proxy variables that abound in real-life scenarios can be leveraged to identify causal effects and therefore facilitate decision-making. Building upon this line of work, we propose a novel optimal individualized treatment regime based on so-called outcome and treatment confounding bridges. We then show that the value function of this new optimal treatment regime is superior to that of existing ones in the literature. Theoretical guarantees, including identification, superiority, excess value bound, and consistency of the estimated regime, are established. Furthermore, we demonstrate the proposed optimal regime via numerical experiments and a real data application.Comment: NeurIPS 202

THREE ESSAYS ON INTERNATIONAL TRANSMISSION OF SHOCKS

Ph.DDOCTOR OF PHILOSOPH

Energy transfer in multi-collision environments; an experimental test of theory: LiH (10;2) in H2(0;0)

We report separate experimental and theoretical studies that follow the equilibration of highly excited LiH (v=10;J=2) in H2 at 680K. Experiments that follow the time evolution of state-tostate population transfer in multi-collision conditions were carried out by Shen and co-workers at Xinjiang University and East China Institute of Science and Technology with µs resolution. At the same time, theoretical computations on the relaxation of this gas mixture were undertaken by McCaffery and co-workers at Sussex University. Rapid, near-resonant, vibration-vibration energy exchange is a marked feature of the initial relaxation process. However, at later stages of ensemble evolution, slower vibration-rotation transfer processes form the dominant relaxation mechanism. The physics of the decay process are complex and, as demonstrated experimentally here, a single exponential expression is unlikely to capture the form of this decay with any accuracy. When these separate studies were complete, the evolution of modal temperatures from the Sussex calculations were compared with experimental measurements of these same quantities from Shanghai and Urumqui. The two sets of data were found to be identical to within experimental and computational error. This constitutes an important experimental validation of the theoretical/computational model developed by the Sussex group and a significant experimental advance by the group of Shen et.al

LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation

Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the industrial setup, where, besides the model, constructing the graph and handling data sparsity also play critical roles in the overall success of the project. In this work, we report how GNN is applied for large-scale e-commerce item retrieval at Shopee. We introduce our simple yet novel and impactful techniques in graph construction, modeling, and handling data skewness. Specifically, we construct high-quality item graphs by combining strong-signal user behaviors with high-precision collaborative filtering (CF) algorithm. We then develop a new GNN architecture named LightSAGE to produce high-quality items' embeddings for vector search. Finally, we design multiple strategies to handle cold-start and long-tail items, which are critical in an advertisement (ads) system. Our models bring improvement in offline evaluations, online A/B tests, and are deployed to the main traffic of Shopee's Recommendation Advertisement system