DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen Jan Teichert-Kluge Philipp Bach Victor Chernozhukov Martin Spindler Suhas Vijaykumar
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.