HiTS Seminar: Elias Bareinboim, 10:00 am, Thursday, March 21, 2019
Causal Inference and Fusion
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.
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
10:00 am – 11:00 am
Thursday, March 21, 2019
Harvard Medical School
200 Longwood Avenue
Warren Alpert Bldg, 5th Floor, Room 563
Boston, MA 02115
Hosted by John Bachman, Ph.D.