We show how a connection to the 'individualized treatment rules' literature in statistics to create asymptotically valid confidence intervals for RCTs that measure the quality of 'clever' intervention policies.
We show how decision-focused learning can improve the fairness-accuracy tradeoff in algorithmic decision-making using a case study on a real-world domain.
We propose an efficient way to implement decision-focused learning for the kinds of RMABs used for intervention planning in public health.
A blog post about the predict-then-optimize paradigm for algorithmic decision-making, and how we can view it as inducing a task-specific loss function in supervised machine learning.
We propose two new innovations to help improve the learning of task-specific loss functions.
We learn task-specific loss functions that, when trained on, allow a predictive model to make better predictions for the given task.