LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original patient information. In this work, we make three contributions. (1) We show that the discrepancy between the data available during training time and inference time can lead to biased estimates of treatment effects. We formalize this issue as an inference time text confounding problem, where confounders are fully observed during training time but only partially available through text at inference time. (2) To address this problem, we propose a novel framework for estimating treatment effects that explicitly accounts for inference time text confounding. Our framework leverages large language models together with a custom doubly robust learner to mitigate biases caused by the inference time text confounding. (3) Through a series of experiments, we demonstrate the effectiveness of our framework in real-world applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Nonparametric Treatment Effect Identification in School Choice
This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment settings, students are subjected to both lottery-driven variation and regr…
DiagnosticregressionDeep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery has evolved separately from inference m…
Causal DiscoveryCausal InferenceDecision MakingMissing ValuesIdentification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach
The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational…
Causal InferenceMultiple Causal Inference with Latent Confounding
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effe…
Causal InferenceCausal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the stron…
Causal Inference