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DoubleMLDeep: Estimation of Causal Effects with Multimodal Data

Author

Listed:
  • Sven Klaassen
  • Jan Teichert-Kluge
  • Philipp Bach
  • Victor Chernozhukov
  • Martin Spindler
  • Suhas Vijaykumar

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.

Suggested Citation

  • Sven Klaassen & Jan Teichert-Kluge & Philipp Bach & Victor Chernozhukov & Martin Spindler & Suhas Vijaykumar, 2024. "DoubleMLDeep: Estimation of Causal Effects with Multimodal Data," Papers 2402.01785, arXiv.org.
    • Handle: RePEc:arx:papers:2402.01785
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    References listed on IDEAS

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    1. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2021. "Deep Neural Networks for Estimation and Inference," Econometrica, Econometric Society, vol. 89(1), pages 181-213, January.
    2. Ye Luo & Martin Spindler & Jannis Kuck, 2016. "High-Dimensional $L_2$Boosting: Rate of Convergence," Papers 1602.08927, arXiv.org, revised Jul 2022.
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