Abstract
Data Science: A Happy Marriage of Quantitative and Qualitative Thinking?
Highlights
‘Well, Xiao-Li, you just did the same—only a quantitative thinker would try to arrange an orderly quantitative-qualitative spectrum!’ Very true
Should qualitative thinking be a part of our comfort zones as well? Here, the term ‘thinking’ is used with the same breadth and depth as it is in computational thinking and inferential thinking, as promoted in the discussion article by Ani Adhikari, John DeNero, and Michael I
Jordan in the last issue of HDSR; in particular, it includes methods and approaches that are guided by such thinking
Summary
Tanweer et al categorizes concepts such as interpretivism, reflexivity, and abductive reasoning as qualitative sensibilities. Translating the knowledge about the average causal effect to the individual causal effects requires attention to a host of qualitative considerations, such as the relevance of the population as proxy to individuals, as well as the questions of how we define/code individuals, how such coding affects the outcome, and how to assess the uncertainty in this translation when each individual is unique As it happens, a second article on causal inference in this issue, “Individualized Decision Making under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox” by Yifan Cui, studies these kinds of problems in making individualized causal assertions and decisions, but without the full information that would allow for the identification of confounding factors at individual levels. This is because the very question of what to inquire into and how to inquire into a problem can never be handled by a purely quantitative process, it is defined (which requires some qualitative thinking!)
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