Behavioral Big Data Research in Healthcare: Talk at ISB
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
October 28, 2018
Repurposing trees for causal research: talk at BU
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:
Yahav, Shmueli, and Mani (2016). "A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data," MIS Quarterly, (40: 4) pp.819-848.
Shmueli and Yahav (2018), "The Forest or the Trees? Tackling Simpson’s Paradox with Classification Trees", Production and Operations Management, vol 27 no 4, pp. 696-716.
Ali Tafti and I presented a poster on Controlling or losing control? Conditioning on covariates in randomized experiments guided by causal structure at the 2018 Conference on Digital Experimentation (CODE) at MIT. We use Pearl's causal diagrams to show which variables can, should, or should not be conditioned on, and illustrate it on 4 well-known digital experiments.
June 15, 2018
Talk at Università di Padova: "Explaining, Predicting, Describing"
I'll be giving a seminar talk today at University of Padova's Department of Statistical Science (Il Dipartimento di Scienze Statistiche dell'Università di Padova) titled Statistical Modeling in 3D: Describing, Explaining and Predicting. In this talk I extend beyond "explain or predict" to also compare and contrast with "describe".
Where: University of Padova, Aula Benvenuti, Campus S. Caterina
When: June 15, 2018, 12:30pm
May 10, 2018
Keynote at Galilee Quality Conference (Israel) on Behavioral Big Data: Why Quality Engineers Should Care?
On May 24, 2018 @ 9:30am, I'll deliver a keynote talk on Behavioral Big Data: Why Quality Engineers Should Care? at the upcoming 10th Galilee Quality Conference at ORT Braude College of Engineering, Israel. The talk will be in English.
On the same day, at 11:20 I will also co-instruct with Ron Kenett a workshop (in Hebrew) on the topic: Information Quality: What have you learned from your data?
Prof. Galit Shmueli, Institute of Service Science, College of Technology Management, National Tsing Hua University, 101 Kuang Fu Road Sec. 2, Hsinchu 30013 Taiwan
