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Causal Data Science: A general framework for data fusion and causal inference
Causal inference is usually dichotomized into two categories, experimental (Fisher, Cox, Cochran) and observational (Neyman, Rubin, Robins, Dawid, Pearl) which, by and large, have evolved and been studied independently. A wide range of problems faced by the current generation of empirical scientists is more demanding. Experimental and observational studies are but two extremes of a rich spectrum of research designs that generate the bulk of the data available in practical, large-scale situations. In typical medical explorations, for example, data from multiple observational and experimental studies are collected from distinct locations, different sampling conditions, and heterogeneous populations. Piecing together these data sources presents a tremendous opportunity to data scientists since the knowledge conveyed by the combined data would not be attainable from any individual source alone.
However, the biases that emerge in heterogeneous environments require a new set of principles and analytical tools. Some of these biases, including confounding, sampling selection, and cross-population (i.e., external validity) biases, have been addressed in isolation, largely in restricted parametric models. In this talk, I will present a general, non-parametric framework for handling these biases and, ultimately, a theoretical solution to the data fusion problem in causal inference tasks. I will further outline the connections of this theory to current challenges in AI and Machine Learning, including fairness analysis, explainability, and reinforcement learning. I’ll end the talk with some reflections on where we are now in the grand scheme of automating the empirical sciences, a project that I call "Causal Data Science.”
Elias Bareinboim is the Director of the Causal Artificial Intelligence (CausalAI) Laboratory. He also serves as an Associate Professor in the Department of Computer Science at Columbia University.