Valid Inference for Treatment Effects under Multimodal Confounding

Martin Spindler, Philipp Bach, Victor Chernozhukov, Sven Klaassen, Jan Teichert-Kluge, Suhas Vijaykumar

Keywords: Causal Inference, Causal Machine Learning, Double Machine Learning, Multimodal Data, Unstructured Data, Inference

Abstract:

This paper provides methods for the valid estimation and inference of treatment effects in the presence of unstructured, multimodal data, namely text and images, as confounders. We develop a neural network architecture that is adapted to the double machine learning (DML) framework, specifically the partially linear model. An additional contribution of our paper is a new method to generate a semi-synthetic dataset which can be used to evaluate the performance of causal effect estimation in the presence of text and images as confounders.
The proposed methods and architectures are evaluated on a correspondingly generated semi-synthetic dataset and compared to standard approaches, highlighting the potential benefit of using text and images directly in causal studies and of our approach. In the experiments, our methods performs well and achieves the nominal coverage. Our findings might be valuable for researchers and practitioners in economics, marketing, finance, medicine and data science in general who are interested in estimating causal quantities using non-traditional data.

Submission Number: 12