Personalized Treatment Effect Estimation from Unstructured Data
This addresses the limitation of existing methods that rely on structured data, enabling causal inference in domains like healthcare where unstructured data is abundant, though it is incremental in improving estimation techniques.
The paper tackles the problem of estimating personalized treatment effects from unstructured data, such as clinical notes or images, by introducing a plug-in method and two bias-corrected estimators that achieve strong empirical performance on benchmark datasets.
Existing methods for estimating personalized treatment effects typically rely on structured covariates, limiting their applicability to unstructured data. Yet, leveraging unstructured data for causal inference has considerable application potential, for instance in healthcare, where clinical notes or medical images are abundant. To this end, we first introduce an approximate 'plug-in' method trained directly on the neural representations of unstructured data. However, when these fail to capture all confounding information, the method may be subject to confounding bias. We therefore introduce two theoretically grounded estimators that leverage structured measurements of the confounders during training, but allow estimating personalized treatment effects purely from unstructured inputs, while avoiding confounding bias. When these structured measurements are only available for a non-representative subset of the data, these estimators may suffer from sampling bias. To address this, we further introduce a regression-based correction that accounts for the non-uniform sampling, assuming the sampling mechanism is known or can be well-estimated. Our experiments on two benchmark datasets show that the plug-in method, directly trainable on large unstructured datasets, achieves strong empirical performance across all settings, despite its simplicity.