<ul data-eligibleForWebStory="false"><li>Estimating treatment effects is crucial in medicine for personalized decision-making but faces challenges due to discrepancies in data available at training and inference times.</li><li>An inference time text confounding problem arises where confounders are fully observed during training but only partially available through text at inference, leading to biased estimates.</li><li>A novel framework is proposed in this work to address the inference time text confounding, leveraging large language models and a custom doubly robust learner to mitigate biases.</li><li>Experiments conducted demonstrate the effectiveness of the framework in real-world applications for estimating treatment effects under inference time text confounding.</li></ul>

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding

Discover more