With the popularity of deep learning algorithms in the scientific literature as well as by their use and development by companies such as Google, Microsoft and Facebook, their deployment in a variety of tasks is rapidly accelerating. Because of this, better mathematical understanding of deep learning, and more broadly, machine learning algorithms in decision making processes are needed. A key innovation that is needed is how to marry machine learning algorithms into causal modelling procedures, which will help algorithms to develop "reasoning capabilities" (e.g., understand why an algorithm is making the decision/prediction that it makes).
The problem that will be addressed in this project is how to model confounders so as to develop causal effect estimators that have desirable sampling properties. In addition, it is important to have well-justified procedures that computationally scale with the number of observations. In this project, the PI and team will focus their research in two areas. The first will be to understand the implications of deep learning algorithms and their performance on foundational assumptions for the popular potential outcomes model. In recent work, the PI discovered a fundamental tension between Gaussian process classification algorithms, covariate overlap and regularity of causal effect estimators. The goal of the first aim of the research will be to see if a similar phenomenon holds for deep learning algorithms. In addition, the interpolation properties of Gaussian process and deep learning-based classification algorithms will be explored. The second part of the project will deal with developing scalable algorithms for causal effect estimation. New computationally scalable algorithms for causal effect estimation will be developed as part of this research.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
