Estimating Identifiable Causal Effects through Double Machine Learning
Keywords:Causality, Graphical Models, Causal Learning
AbstractIdentifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.
How to Cite
Jung, Y., Tian, J., & Bareinboim, E. (2021). Estimating Identifiable Causal Effects through Double Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12113-12122. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17438
AAAI Technical Track on Reasoning under Uncertainty