In this paper, Joshua proposes a new deep learning model to advance causal inference analysis of clinical trials. Driven by the pursuit of better treatment options, it represents a culmination of his own research and the work of organizations like the International Progressive MS Alliance to leverage the power of technology to improve patient outcomes. One of the primary challenges of using causal and counterfactual inference in this scenario is missing data from one branch of the trial. For example, if a patient has received only one type of treatment throughout their observation period, it is difficult to predict how they might respond to alternative treatments. The goal is to construct deep models capable of identifying when sufficient data is available for making these predictions. But why is this work so important? The answer lies in the sheer complexity of clinical data. Researchers often encounter patients with unique characteristics not seen in previous trials. These 8 DAILY MICCAI Tuesday Poster Presentation Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models Joshua Durso-Finley is a PhD student in the Probabilistic Vision Group at McGill University under the supervision of Tal Arbel. His work, exploring the convergence of deep learning and causal inference, reshapes how we analyze clinical trials. He speaks to us ahead of his poster this afternoon.
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