There’s so much we don’t know
but we’ve been brainwashed to think that we do.
Last Sunday, the 2019 Women in Data Science Taipei conference took place. I gave a talk on "Behavioral Big Data & Healthcare Analytics" and then participated in the "Roundtable + Networking Session" which constituted of folks surrounding each speaker and asking lots of open questions. It was a highly energetic event with lots of engagement from female and male participants, and a diverse audience from industry and universities.
I am delighted to receive the 2018 Outstanding Mentor Award by the NTHU College of Technology Management. The real award is the opportunity to work with so many wonderful students and colleagues!
Mentoring is an incredibly important relationship, which sows many seeds in both the mentor and the mentee. This award comes at a very meaningful (and sad) time, with the unexpected passing away of my beloved graduate advisor Prof. Aya Cohen (for whom we created a commemorating blog). Knowing how important she's been in my life, receiving the mentoring award is extremely meaningful to me.
Our paper "PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research" by Sharma et al, (forthcoming in JAIS) examines the use of information criteria for model selection, such as AIC, BIC and related metrics, in the context of PLS-SEM models (partial least squares path models). Our co-author Marko Sarstedt presents the main points and results in a slick 10-min video.
Today I'll deliver a talk as part of the 2019 SRITNE Distinguished Speakers series on Behavioral Big Data Research in Healthcare: Challenges and Opportunities
Where: Indian School of Business, AC4 MLT
When: Feb 7, 2019, 3pm
I'll be giving a talk on Monday, Oct 29, 2018, at Boston University's Questrom School of Business on Repurposing trees for causal research.
Abstract:
Classification & Regression Trees ("trees") and their variants are popular predictive tools used in many machine learning applications and predictive research. While studying causal effects and structures is central to research in many areas, trees are not commonly used in causal-explanatory research. In this talk I will describe special uses of trees that we developed for tackling two causal-explanatory issues: self selection and confounder detection. For self selection, we develop a novel tree-based approach adjusting for observable self-selection bias in intervention studies, thereby creating a useful tool for analysis of observational impact studies as well as post-analysis of experimental data which scales for big data. For tackling confounders, we use trees for automated detection of potential Simpson's paradoxes in data with few or many potential confounding variables, and even with large samples (big data). Our approach relies on the tree structure and the location of the cause vs. the confounders in the tree. I will illustrate these approaches on applications in eGov, labor economics, and healthcare.
Relevant papers: