DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Abstract
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose 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 the semi-synthetic dataset and compared to standard approaches, highlighting the potential benefit of using text and images directly in causal studies. Our findings have implications 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.
Cite
@article{arxiv.2402.01785,
title = {DoubleMLDeep: Estimation of Causal Effects with Multimodal Data},
author = {Sven Klaassen and Jan Teichert-Kluge and Philipp Bach and Victor Chernozhukov and Martin Spindler and Suhas Vijaykumar},
journal= {arXiv preprint arXiv:2402.01785},
year = {2024}
}