To join via Zoom: To join this seminar, please request Zoom connection details from headsec [at] stat.ubc.ca.
Presentation 1
Time: 11:00am – 11:30am
Speaker: Grace Yin, UBC Statistics MSc student
Title: On the Relationship between Predictive Models and Structural Causal Model Learning
Abstract: Structural Causal Models (SCMs) are a key component in causal inference and they have been used for a long time in many fields. We proposed an approach to identify the structure of an SCM based on a constant risk theorem. In particular, we proved that for an equivariant model and predictor, the risk function is constant across interventions described by the action of a group. These theories give a straightforward understanding of certain types of causal model identification. We also explored the risks on a specific SCM for linear regression predictive models with different types of interventions through simulation experiments.
Presentation 2
Time: 11:30am – 12:00pm
Speaker: Wei Tang, UBC Statistics MSc student
Title: Improved model training for multi-horizon time series forecasting in the context of COVID-19
Abstract: Predicting how the Covid-19 pandemic evolves in the future is very important for public health workers and policymakers to prepare for it. The prediction task is a multi-step time series forecasting task. In this report, we compared two commonly used strategies, the iterative and direct strategy, for this task on the Covid dataset under various experiment settings. We further enhanced the two strategies with ideas from K-Nearest Neighborhood (KNN) algorithm to construct the training dataset and proposed an improved iterative strategy which we call the dynamic iterative strategy. The proposed KNN enhanced forecasting strategies are significantly better than the native iterative and direct strategies and achieve satisfying prediction accuracy on the covid prediction challenge at the Covid-19 Forecasting Hub.