Explain-Predict

To Explain or To Predict?

My research examines the fundamental and practical differences between using statistical and other empirical methods for prediction compared to causal explanation and to description. Although the discussion of explanation vs. prediction has been actively pursued in the philosophy of science, the statistics literature has not considered it in a holistic way. Yet, statistical modeling can be and is used for each of these goals.

Two upcoming talks at CityU Hong Kong

This week I will give two talks at the Information System Department, College of Business, City University of Hong Kong:

To Explain or To Predict?
Date: 17 Jan, 2017
Time: 2:00pm to 3:30pm
Venue: AC3-6-208, Academic Building 3, City University of Hong Kong

A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data
Date: 18 Jan, 2017
Time: 2:00pm to 3:30pm
Venue: AC3-7-211, Academic Building 3, City University of Hong Kong

"To Explain or To Predict?" at NTHU's Institute of Information Systems & Applications

On Wednesday (March 23) I'll talk about the difference between using statistical models and data mining for explanation vs. prediction at NTHU's Institute of Information Systems & Applications (here's the official announcement). For those who've been waiting to hear this talk in Hsinchu, here's your chance. Looking forward to a lively discussion.
Location: NTHU, Delta Building, Room 105
Date & Time: March 23, 13:30-15:00

Big Data - To Explain or To Predict? @ U Toronto's Rotman School of Mgmt

On Friday, March 4 (2016), I'll deliver a talk on Big Data - To Explain or To Predict as part of the Big Data Experts Speaker Series @ Rotman School of Management, University of Toronto. The talk will discuss the differences between modeling data for causal explanation vs. prediction, with the aim of clarifying usages of big data analytics in both academic research and industry.

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