Causal Inference and Data Fusion

Abstract: Causal inference is usually dichotomized into two categories, experimental (Fisher, Cox, Cochran) and observational (Neyman, Rubin, Robins, Dawid, Pearl) which, by and large, are studied separately. Understanding reality 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 observations and experiments are collected, coming from distinct experimental setups, different sampling conditions, and heterogeneous populations.

In this talk, I will introduce the data-fusion problem, which is concerned with piecing together multiple datasets collected under heterogeneous conditions (to be defined) so as to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to causal analysts since the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. I will present my work on a general, non-parametric framework for handling these biases and, ultimately, a theoretical solution to the problem of fusion in causal inference tasks.

Suggested readings:

[1] E. Bareinboim and J. Pearl.   Causal inference and the Data-Fusion Problem.   Proceedings of the National Academy of Sciences, 113(27): 7345-7352, 2016.  https://www.pnas.org/content/113/27/7345

[2] E. Bareinboim, J. Correa, D. Ibeling, T. Icard. On Pearl’s Hierarchy and the Foundations of Causal Inference. In “Probabilistic and Causal Inference: The Works of Judea Pearl”, In Probabilistic and Causal Inference: The Works of Judea Pearl (ACM, Special Turing Series), pp. 507-556, 2022.  https://causalai.net/r60.pdf

[3]  K. Xia, K. Lee, Y. Bengio, E. Bareinboim. The Causal-Neural Connection: Expressiveness, Learnability, and Inference In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.  https://causalai.net/r80.pdf

About the speaker: Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. His work was the first to propose a general solution to the problem of “data-fusion,” providing practical methods for combining datasets generated under different experimental conditions and plagued with various biases. More recently, Bareinboim has been exploring the intersection of causal inference with decision-making (including reinforcement learning) and explainability (including fairness analysis). Bareinboim received his Ph.D. from the University of California, Los Angeles, where he was advised by Judea Pearl. Bareinboim was named one of “AI’s 10 to Watch” by IEEE, and is a recipient of the NSF CAREER Award, the ONR Young Investigator Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award.